idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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49,401 | Random forest cross validation for feature selection, imbalanced datasets | Your class1 and class2 summed 588 and 4709 do not add up to 5267 but 5297.
But assuming you have a 5297x26 set of regressors allows me to estimate the random forrest by the call you posted
data <- data.frame(matrix(rnorm(5297*26), ncol=26),
label=c(rep('class1', 588),
re... | Random forest cross validation for feature selection, imbalanced datasets | Your class1 and class2 summed 588 and 4709 do not add up to 5267 but 5297.
But assuming you have a 5297x26 set of regressors allows me to estimate the random forrest by the call you posted
data <- da | Random forest cross validation for feature selection, imbalanced datasets
Your class1 and class2 summed 588 and 4709 do not add up to 5267 but 5297.
But assuming you have a 5297x26 set of regressors allows me to estimate the random forrest by the call you posted
data <- data.frame(matrix(rnorm(5297*26), ncol=26),
... | Random forest cross validation for feature selection, imbalanced datasets
Your class1 and class2 summed 588 and 4709 do not add up to 5267 but 5297.
But assuming you have a 5297x26 set of regressors allows me to estimate the random forrest by the call you posted
data <- da |
49,402 | Random forest cross validation for feature selection, imbalanced datasets | Any method that requires that you discard data in order to use it is defective. You may have been tempted to do this because you intend to use a discontinuous improper accuracy scoring rule such as proportion "classified" "correctly". That particular accuracy score is arbitrarily manipulated by the prevalence of posi... | Random forest cross validation for feature selection, imbalanced datasets | Any method that requires that you discard data in order to use it is defective. You may have been tempted to do this because you intend to use a discontinuous improper accuracy scoring rule such as p | Random forest cross validation for feature selection, imbalanced datasets
Any method that requires that you discard data in order to use it is defective. You may have been tempted to do this because you intend to use a discontinuous improper accuracy scoring rule such as proportion "classified" "correctly". That part... | Random forest cross validation for feature selection, imbalanced datasets
Any method that requires that you discard data in order to use it is defective. You may have been tempted to do this because you intend to use a discontinuous improper accuracy scoring rule such as p |
49,403 | Time Series Forecasting vs Linear Regression Extrapolation | The main reason why using ordinary least squares regression is frowned upon in modeling time series data is that the error terms are correlated with each other (this is called autocorrelation). If this is the case, then your standard errors from OLS will be incorrect, which affects hypothesis testing.
There are several... | Time Series Forecasting vs Linear Regression Extrapolation | The main reason why using ordinary least squares regression is frowned upon in modeling time series data is that the error terms are correlated with each other (this is called autocorrelation). If thi | Time Series Forecasting vs Linear Regression Extrapolation
The main reason why using ordinary least squares regression is frowned upon in modeling time series data is that the error terms are correlated with each other (this is called autocorrelation). If this is the case, then your standard errors from OLS will be inc... | Time Series Forecasting vs Linear Regression Extrapolation
The main reason why using ordinary least squares regression is frowned upon in modeling time series data is that the error terms are correlated with each other (this is called autocorrelation). If thi |
49,404 | VIF calculation in regression | It is important to address multicollinearity within all the explanatory variables, as there can be linear correlation between a group of variables (three or more) but none among all their possible pairs.
The threshold for discarding explanatory variables with the Variance Inflation Factor is subjective. Here is a recom... | VIF calculation in regression | It is important to address multicollinearity within all the explanatory variables, as there can be linear correlation between a group of variables (three or more) but none among all their possible pai | VIF calculation in regression
It is important to address multicollinearity within all the explanatory variables, as there can be linear correlation between a group of variables (three or more) but none among all their possible pairs.
The threshold for discarding explanatory variables with the Variance Inflation Factor ... | VIF calculation in regression
It is important to address multicollinearity within all the explanatory variables, as there can be linear correlation between a group of variables (three or more) but none among all their possible pai |
49,405 | Inter-rater agreement for Likert scale | Krippendorff's alpha, originally developed in the field of content analysis, is well-suited for dealing with ordinal ratings such as Likert-scale ratings. It has several advantages over some other measures such as Cohen's Kappa, Fleiss's Kappa, Cronbach's alpha: it is capable of dealing with more than 2 raters; it is r... | Inter-rater agreement for Likert scale | Krippendorff's alpha, originally developed in the field of content analysis, is well-suited for dealing with ordinal ratings such as Likert-scale ratings. It has several advantages over some other mea | Inter-rater agreement for Likert scale
Krippendorff's alpha, originally developed in the field of content analysis, is well-suited for dealing with ordinal ratings such as Likert-scale ratings. It has several advantages over some other measures such as Cohen's Kappa, Fleiss's Kappa, Cronbach's alpha: it is capable of d... | Inter-rater agreement for Likert scale
Krippendorff's alpha, originally developed in the field of content analysis, is well-suited for dealing with ordinal ratings such as Likert-scale ratings. It has several advantages over some other mea |
49,406 | Logistic regression with time series predictor data | Your question sounds very much like you are interested in discrete time event history analysis (aka discrete time survival analysis, aka a logit hazard model) to answer the question whether and when will an event occur?
For example, equation 1 gives the logit hazard where discrete time periods (up to period $T$ are ind... | Logistic regression with time series predictor data | Your question sounds very much like you are interested in discrete time event history analysis (aka discrete time survival analysis, aka a logit hazard model) to answer the question whether and when w | Logistic regression with time series predictor data
Your question sounds very much like you are interested in discrete time event history analysis (aka discrete time survival analysis, aka a logit hazard model) to answer the question whether and when will an event occur?
For example, equation 1 gives the logit hazard w... | Logistic regression with time series predictor data
Your question sounds very much like you are interested in discrete time event history analysis (aka discrete time survival analysis, aka a logit hazard model) to answer the question whether and when w |
49,407 | A question on a non-parametric estimating equation | There is an issue here, nicely captured by the latest comment to the question:
Doesn't [Equation] 10.5.6 ask us how much $Y$ has to shift so that the median of $Y$ coincides with the median of $n=n_1+n_2$ values? Why is it the median of $X$ instead?
When the meaning of the equation is parsed--translated from math-ese... | A question on a non-parametric estimating equation | There is an issue here, nicely captured by the latest comment to the question:
Doesn't [Equation] 10.5.6 ask us how much $Y$ has to shift so that the median of $Y$ coincides with the median of $n=n_1 | A question on a non-parametric estimating equation
There is an issue here, nicely captured by the latest comment to the question:
Doesn't [Equation] 10.5.6 ask us how much $Y$ has to shift so that the median of $Y$ coincides with the median of $n=n_1+n_2$ values? Why is it the median of $X$ instead?
When the meaning ... | A question on a non-parametric estimating equation
There is an issue here, nicely captured by the latest comment to the question:
Doesn't [Equation] 10.5.6 ask us how much $Y$ has to shift so that the median of $Y$ coincides with the median of $n=n_1 |
49,408 | Precision and recall are equal when the size is same | Let's call the number of users who are correctly classified as experts by $tp$ (true positive), the number of users who are incorrectly classified as non-experts (but they are experts) by $fn$ (false negative), and the number who are incorrectly classified as experts (because they are not) by $fp$ (false positive).
The... | Precision and recall are equal when the size is same | Let's call the number of users who are correctly classified as experts by $tp$ (true positive), the number of users who are incorrectly classified as non-experts (but they are experts) by $fn$ (false | Precision and recall are equal when the size is same
Let's call the number of users who are correctly classified as experts by $tp$ (true positive), the number of users who are incorrectly classified as non-experts (but they are experts) by $fn$ (false negative), and the number who are incorrectly classified as experts... | Precision and recall are equal when the size is same
Let's call the number of users who are correctly classified as experts by $tp$ (true positive), the number of users who are incorrectly classified as non-experts (but they are experts) by $fn$ (false |
49,409 | Formula to calculate beta matrix in multivariate analysis [duplicate] | I think I understand what you're asking, but correct me if I'm wrong. The analytical formula for $\beta$ is the same for the multivariate case as the univariate case:
$$
\hat \beta = (X'X)^{-1}X'Y
$$
You find this the same way as for the univariate case, by taking the first derivative of residual sum of squares. It i... | Formula to calculate beta matrix in multivariate analysis [duplicate] | I think I understand what you're asking, but correct me if I'm wrong. The analytical formula for $\beta$ is the same for the multivariate case as the univariate case:
$$
\hat \beta = (X'X)^{-1}X'Y
$$ | Formula to calculate beta matrix in multivariate analysis [duplicate]
I think I understand what you're asking, but correct me if I'm wrong. The analytical formula for $\beta$ is the same for the multivariate case as the univariate case:
$$
\hat \beta = (X'X)^{-1}X'Y
$$
You find this the same way as for the univariate ... | Formula to calculate beta matrix in multivariate analysis [duplicate]
I think I understand what you're asking, but correct me if I'm wrong. The analytical formula for $\beta$ is the same for the multivariate case as the univariate case:
$$
\hat \beta = (X'X)^{-1}X'Y
$$ |
49,410 | Formula to calculate beta matrix in multivariate analysis [duplicate] | If you have $q$ equations and $p$ independent variables (including a constant) that appear in every equation, the parameter estimates are given by the $p \times q$ matrix:
$$M=(X'IX)^{-1}X'IY$$
where
$Y$ is $n \times q$ matrix of dependent variables
X is $n \times p$ matrix of covariates
I is the identity matrix | Formula to calculate beta matrix in multivariate analysis [duplicate] | If you have $q$ equations and $p$ independent variables (including a constant) that appear in every equation, the parameter estimates are given by the $p \times q$ matrix:
$$M=(X'IX)^{-1}X'IY$$
where
| Formula to calculate beta matrix in multivariate analysis [duplicate]
If you have $q$ equations and $p$ independent variables (including a constant) that appear in every equation, the parameter estimates are given by the $p \times q$ matrix:
$$M=(X'IX)^{-1}X'IY$$
where
$Y$ is $n \times q$ matrix of dependent variables... | Formula to calculate beta matrix in multivariate analysis [duplicate]
If you have $q$ equations and $p$ independent variables (including a constant) that appear in every equation, the parameter estimates are given by the $p \times q$ matrix:
$$M=(X'IX)^{-1}X'IY$$
where
|
49,411 | Formula to calculate beta matrix in multivariate analysis [duplicate] | Other answers nicely cover how to derive the $\beta$ coefficients. I'm not sure what you mean by $n, \beta$ "put together." But, if it means that you'd like to use the coefficients to derive the model's predicted values using the coefficients, it's simply the product $XB$, where $X$ is an $m \times n$ matrix of $m$ obs... | Formula to calculate beta matrix in multivariate analysis [duplicate] | Other answers nicely cover how to derive the $\beta$ coefficients. I'm not sure what you mean by $n, \beta$ "put together." But, if it means that you'd like to use the coefficients to derive the model | Formula to calculate beta matrix in multivariate analysis [duplicate]
Other answers nicely cover how to derive the $\beta$ coefficients. I'm not sure what you mean by $n, \beta$ "put together." But, if it means that you'd like to use the coefficients to derive the model's predicted values using the coefficients, it's s... | Formula to calculate beta matrix in multivariate analysis [duplicate]
Other answers nicely cover how to derive the $\beta$ coefficients. I'm not sure what you mean by $n, \beta$ "put together." But, if it means that you'd like to use the coefficients to derive the model |
49,412 | Variance of $X_i / \sum\limits_{j=1}^n X_j$ | I don't think this question can be fully answered without more information. But we can find a few quantities. Let $n=N$ and define $Y_i={{X_i} \over {\sum_{j=1}^N X_j}}.$ Let the variance of $Y_i$ be $\sigma^2$ and the covariance be $\sigma_{Y_iY_j}=\sigma_{12}.$ We can do this since the $Y_i$ are identically distribut... | Variance of $X_i / \sum\limits_{j=1}^n X_j$ | I don't think this question can be fully answered without more information. But we can find a few quantities. Let $n=N$ and define $Y_i={{X_i} \over {\sum_{j=1}^N X_j}}.$ Let the variance of $Y_i$ be | Variance of $X_i / \sum\limits_{j=1}^n X_j$
I don't think this question can be fully answered without more information. But we can find a few quantities. Let $n=N$ and define $Y_i={{X_i} \over {\sum_{j=1}^N X_j}}.$ Let the variance of $Y_i$ be $\sigma^2$ and the covariance be $\sigma_{Y_iY_j}=\sigma_{12}.$ We can do th... | Variance of $X_i / \sum\limits_{j=1}^n X_j$
I don't think this question can be fully answered without more information. But we can find a few quantities. Let $n=N$ and define $Y_i={{X_i} \over {\sum_{j=1}^N X_j}}.$ Let the variance of $Y_i$ be |
49,413 | Derivation of uncertainty propagation? | The idea behind the differential calculus is to study potentially complicated functions $f:\mathbb{R}^n \to \mathbb{R}^m$ by means of linear approximations. Everything flows from this single idea.
For $x\in \mathbb{R}^n$ "the" linear approximation to $f$ near $x$ (if a unique one exists) is called the "derivative" or... | Derivation of uncertainty propagation? | The idea behind the differential calculus is to study potentially complicated functions $f:\mathbb{R}^n \to \mathbb{R}^m$ by means of linear approximations. Everything flows from this single idea.
F | Derivation of uncertainty propagation?
The idea behind the differential calculus is to study potentially complicated functions $f:\mathbb{R}^n \to \mathbb{R}^m$ by means of linear approximations. Everything flows from this single idea.
For $x\in \mathbb{R}^n$ "the" linear approximation to $f$ near $x$ (if a unique on... | Derivation of uncertainty propagation?
The idea behind the differential calculus is to study potentially complicated functions $f:\mathbb{R}^n \to \mathbb{R}^m$ by means of linear approximations. Everything flows from this single idea.
F |
49,414 | Using log-linear models for presence/absence data in wildlife | You can use the binomial GLM, as it provides the freedom to model different sample sizes, $m_i$. So, you can use glm() function as follows:
glm(cbind(presence, absence) ~ 1 + treatment + year, family=binomial)
where "presence" and "absence" show the number of present or absent cases. | Using log-linear models for presence/absence data in wildlife | You can use the binomial GLM, as it provides the freedom to model different sample sizes, $m_i$. So, you can use glm() function as follows:
glm(cbind(presence, absence) ~ 1 + treatment + year, family= | Using log-linear models for presence/absence data in wildlife
You can use the binomial GLM, as it provides the freedom to model different sample sizes, $m_i$. So, you can use glm() function as follows:
glm(cbind(presence, absence) ~ 1 + treatment + year, family=binomial)
where "presence" and "absence" show the number ... | Using log-linear models for presence/absence data in wildlife
You can use the binomial GLM, as it provides the freedom to model different sample sizes, $m_i$. So, you can use glm() function as follows:
glm(cbind(presence, absence) ~ 1 + treatment + year, family= |
49,415 | Using log-linear models for presence/absence data in wildlife | I highly recommend you the R book, chapters 15 till 17.
If you have just categorical variables and no continuous ones, Crawley' R Book suggests to make a contingency table or to convert your binary data in proportion data and analyze it then.
I had the same problem (binary count data and just categorical explanatory v... | Using log-linear models for presence/absence data in wildlife | I highly recommend you the R book, chapters 15 till 17.
If you have just categorical variables and no continuous ones, Crawley' R Book suggests to make a contingency table or to convert your binary da | Using log-linear models for presence/absence data in wildlife
I highly recommend you the R book, chapters 15 till 17.
If you have just categorical variables and no continuous ones, Crawley' R Book suggests to make a contingency table or to convert your binary data in proportion data and analyze it then.
I had the same... | Using log-linear models for presence/absence data in wildlife
I highly recommend you the R book, chapters 15 till 17.
If you have just categorical variables and no continuous ones, Crawley' R Book suggests to make a contingency table or to convert your binary da |
49,416 | Can a deep belief network (stacked RBMS) be used solely as a dataset generator? | Note that initialising neural nets with DBNs is more of a historical anecdote nowadays. Direct supervised training with dropout regularization and piecewise linear activation functions tend to work much better in the presence of many labeled training examples.
More direct answer to your question:
1) Using DBN features ... | Can a deep belief network (stacked RBMS) be used solely as a dataset generator? | Note that initialising neural nets with DBNs is more of a historical anecdote nowadays. Direct supervised training with dropout regularization and piecewise linear activation functions tend to work mu | Can a deep belief network (stacked RBMS) be used solely as a dataset generator?
Note that initialising neural nets with DBNs is more of a historical anecdote nowadays. Direct supervised training with dropout regularization and piecewise linear activation functions tend to work much better in the presence of many labele... | Can a deep belief network (stacked RBMS) be used solely as a dataset generator?
Note that initialising neural nets with DBNs is more of a historical anecdote nowadays. Direct supervised training with dropout regularization and piecewise linear activation functions tend to work mu |
49,417 | Who said, "let the data speak for themselves"? | The earliest I can find in Google Books is from The Lookout, Seamen's Church Institute of New York and New Jersey, 1915 so unlikely to be John Tukey, who was born that year.
A very similar quotation appears to come from records of the Protestant Episcopal Church in the United States of America in 1917 so it is possible... | Who said, "let the data speak for themselves"? | The earliest I can find in Google Books is from The Lookout, Seamen's Church Institute of New York and New Jersey, 1915 so unlikely to be John Tukey, who was born that year.
A very similar quotation a | Who said, "let the data speak for themselves"?
The earliest I can find in Google Books is from The Lookout, Seamen's Church Institute of New York and New Jersey, 1915 so unlikely to be John Tukey, who was born that year.
A very similar quotation appears to come from records of the Protestant Episcopal Church in the Uni... | Who said, "let the data speak for themselves"?
The earliest I can find in Google Books is from The Lookout, Seamen's Church Institute of New York and New Jersey, 1915 so unlikely to be John Tukey, who was born that year.
A very similar quotation a |
49,418 | Who said, "let the data speak for themselves"? | For what it's worth, Jaynes (Logic of Science, 2003) attributes it to Fisher:
R. A. Fisher’s maxim: ‘Let
the data speak for themselves!’ which has so dominated statistics in this century. The data
cannot speak for themselves; and they never have, in any real problem of inference. | Who said, "let the data speak for themselves"? | For what it's worth, Jaynes (Logic of Science, 2003) attributes it to Fisher:
R. A. Fisher’s maxim: ‘Let
the data speak for themselves!’ which has so dominated statistics in this century. The data
ca | Who said, "let the data speak for themselves"?
For what it's worth, Jaynes (Logic of Science, 2003) attributes it to Fisher:
R. A. Fisher’s maxim: ‘Let
the data speak for themselves!’ which has so dominated statistics in this century. The data
cannot speak for themselves; and they never have, in any real problem of in... | Who said, "let the data speak for themselves"?
For what it's worth, Jaynes (Logic of Science, 2003) attributes it to Fisher:
R. A. Fisher’s maxim: ‘Let
the data speak for themselves!’ which has so dominated statistics in this century. The data
ca |
49,419 | Who said, "let the data speak for themselves"? | Google finds this paper on pubmed, with this exact quote in title which attributes it to Aristotle and Newton:
This contrasts sharply with the second approach suggested by Aristotle and revived by Newton in the 18th century that places data at its center: "Let the data speak for themselves." | Who said, "let the data speak for themselves"? | Google finds this paper on pubmed, with this exact quote in title which attributes it to Aristotle and Newton:
This contrasts sharply with the second approach suggested by Aristotle and revived by N | Who said, "let the data speak for themselves"?
Google finds this paper on pubmed, with this exact quote in title which attributes it to Aristotle and Newton:
This contrasts sharply with the second approach suggested by Aristotle and revived by Newton in the 18th century that places data at its center: "Let the data s... | Who said, "let the data speak for themselves"?
Google finds this paper on pubmed, with this exact quote in title which attributes it to Aristotle and Newton:
This contrasts sharply with the second approach suggested by Aristotle and revived by N |
49,420 | Hypothesis testing with Neyman–Pearson (finding cutoff quantile of Poisson dist) | Apart from a few oddnesses with notation, you seem to have almost got it sorted out.
This bit is wrong, though:
Now to find the value of k where we would reject H0 when the total number of observed errors is no greater than k, can we not just use R with the following code:
qpois(0.05 = the alpha level,5 = value of nu... | Hypothesis testing with Neyman–Pearson (finding cutoff quantile of Poisson dist) | Apart from a few oddnesses with notation, you seem to have almost got it sorted out.
This bit is wrong, though:
Now to find the value of k where we would reject H0 when the total number of observed e | Hypothesis testing with Neyman–Pearson (finding cutoff quantile of Poisson dist)
Apart from a few oddnesses with notation, you seem to have almost got it sorted out.
This bit is wrong, though:
Now to find the value of k where we would reject H0 when the total number of observed errors is no greater than k, can we not ... | Hypothesis testing with Neyman–Pearson (finding cutoff quantile of Poisson dist)
Apart from a few oddnesses with notation, you seem to have almost got it sorted out.
This bit is wrong, though:
Now to find the value of k where we would reject H0 when the total number of observed e |
49,421 | Difference between point-biserial and rank-biserial correlations | The Wikipedia formula of "rank-biserial correlation" that you show was introduced by Glass (1966) and it is not equivalent to usual Pearson $r$ when the latter is computed on ranks data (that is, $r$ which actually will be Spearman's $rho$).
Let define $Y$ to be the quantitative variable already turned into ranks; and ... | Difference between point-biserial and rank-biserial correlations | The Wikipedia formula of "rank-biserial correlation" that you show was introduced by Glass (1966) and it is not equivalent to usual Pearson $r$ when the latter is computed on ranks data (that is, $r$ | Difference between point-biserial and rank-biserial correlations
The Wikipedia formula of "rank-biserial correlation" that you show was introduced by Glass (1966) and it is not equivalent to usual Pearson $r$ when the latter is computed on ranks data (that is, $r$ which actually will be Spearman's $rho$).
Let define $Y... | Difference between point-biserial and rank-biserial correlations
The Wikipedia formula of "rank-biserial correlation" that you show was introduced by Glass (1966) and it is not equivalent to usual Pearson $r$ when the latter is computed on ranks data (that is, $r$ |
49,422 | Logit with dummies when certain number of dummies must be used | If the additive model's a good fit there isn't any problem. You could use it to make predictions for success of the process with six, eight or two dozen workers; but you needn't, just as if the ambient temperature were a predictor you shouldn't be tempted to use the model to predict success of the process at -50°C or 3... | Logit with dummies when certain number of dummies must be used | If the additive model's a good fit there isn't any problem. You could use it to make predictions for success of the process with six, eight or two dozen workers; but you needn't, just as if the ambien | Logit with dummies when certain number of dummies must be used
If the additive model's a good fit there isn't any problem. You could use it to make predictions for success of the process with six, eight or two dozen workers; but you needn't, just as if the ambient temperature were a predictor you shouldn't be tempted t... | Logit with dummies when certain number of dummies must be used
If the additive model's a good fit there isn't any problem. You could use it to make predictions for success of the process with six, eight or two dozen workers; but you needn't, just as if the ambien |
49,423 | Monte Carlo simulation vs. machine learning algorithms: what is the difference in application? [closed] | MC is not an inference technique for finding the "best" model, it is a numerical tool to obtain samples from a given model. Sure enough you can also build inference procedures relying on MC (e.g. optimizing a criterion over parameters as a function of the simulated empirical distribution) but that doesn't change the re... | Monte Carlo simulation vs. machine learning algorithms: what is the difference in application? [clos | MC is not an inference technique for finding the "best" model, it is a numerical tool to obtain samples from a given model. Sure enough you can also build inference procedures relying on MC (e.g. opti | Monte Carlo simulation vs. machine learning algorithms: what is the difference in application? [closed]
MC is not an inference technique for finding the "best" model, it is a numerical tool to obtain samples from a given model. Sure enough you can also build inference procedures relying on MC (e.g. optimizing a criteri... | Monte Carlo simulation vs. machine learning algorithms: what is the difference in application? [clos
MC is not an inference technique for finding the "best" model, it is a numerical tool to obtain samples from a given model. Sure enough you can also build inference procedures relying on MC (e.g. opti |
49,424 | How would you visualize the difference between Cox/Weibull regression? | You can try R's visreg package, as described in this paper "Visualization of Regression Models Using visreg". The package interface is consistent for visualizing linear models, generalized linear models, proportional hazards models, generalized additive models, robust regression models and more. Page 12 has an example... | How would you visualize the difference between Cox/Weibull regression? | You can try R's visreg package, as described in this paper "Visualization of Regression Models Using visreg". The package interface is consistent for visualizing linear models, generalized linear mod | How would you visualize the difference between Cox/Weibull regression?
You can try R's visreg package, as described in this paper "Visualization of Regression Models Using visreg". The package interface is consistent for visualizing linear models, generalized linear models, proportional hazards models, generalized add... | How would you visualize the difference between Cox/Weibull regression?
You can try R's visreg package, as described in this paper "Visualization of Regression Models Using visreg". The package interface is consistent for visualizing linear models, generalized linear mod |
49,425 | Coefficient sign changes in fixed effect and first-difference estimation | Another thing that could go awry is an unbalanced panel where you have "gaps" in the middle of the time series. The FD estimator will lose two observations if there a single period missing. The dummy approach will loose only one. Are the sample sizes wildly different between the two regressions by any chance? What happ... | Coefficient sign changes in fixed effect and first-difference estimation | Another thing that could go awry is an unbalanced panel where you have "gaps" in the middle of the time series. The FD estimator will lose two observations if there a single period missing. The dummy | Coefficient sign changes in fixed effect and first-difference estimation
Another thing that could go awry is an unbalanced panel where you have "gaps" in the middle of the time series. The FD estimator will lose two observations if there a single period missing. The dummy approach will loose only one. Are the sample si... | Coefficient sign changes in fixed effect and first-difference estimation
Another thing that could go awry is an unbalanced panel where you have "gaps" in the middle of the time series. The FD estimator will lose two observations if there a single period missing. The dummy |
49,426 | Higher $r^2$ value on test data than training data? | I think the formula to calculate r-squared is
R-squared = 1 - (RSS/TSS)
where TSS = sum((y-mean(y))^2) and RSS = sum((y-y.predict)^2) | Higher $r^2$ value on test data than training data? | I think the formula to calculate r-squared is
R-squared = 1 - (RSS/TSS)
where TSS = sum((y-mean(y))^2) and RSS = sum((y-y.predict)^2) | Higher $r^2$ value on test data than training data?
I think the formula to calculate r-squared is
R-squared = 1 - (RSS/TSS)
where TSS = sum((y-mean(y))^2) and RSS = sum((y-y.predict)^2) | Higher $r^2$ value on test data than training data?
I think the formula to calculate r-squared is
R-squared = 1 - (RSS/TSS)
where TSS = sum((y-mean(y))^2) and RSS = sum((y-y.predict)^2) |
49,427 | Higher $r^2$ value on test data than training data? | $R^2$ value is not a metric for model selection or model fit.
The reason for this is that there is inherent variability of data may affect the $R^2$.
Consider the following data sets:
The (Y_ v/s X) plot has more spread than (Y v/s X). As a result the $R^2$ value for the previous(which has more variance) will be lowe... | Higher $r^2$ value on test data than training data? | $R^2$ value is not a metric for model selection or model fit.
The reason for this is that there is inherent variability of data may affect the $R^2$.
Consider the following data sets:
The (Y_ v/s X) | Higher $r^2$ value on test data than training data?
$R^2$ value is not a metric for model selection or model fit.
The reason for this is that there is inherent variability of data may affect the $R^2$.
Consider the following data sets:
The (Y_ v/s X) plot has more spread than (Y v/s X). As a result the $R^2$ value fo... | Higher $r^2$ value on test data than training data?
$R^2$ value is not a metric for model selection or model fit.
The reason for this is that there is inherent variability of data may affect the $R^2$.
Consider the following data sets:
The (Y_ v/s X) |
49,428 | Higher $r^2$ value on test data than training data? | One explanation might relate to how you subset your test data (they way you split training and testing data). If your test data only consists of (just a few) similar observations then it is very likely for your R-squared measure to be different than that of the training data.
A good practice is to split X% of the data ... | Higher $r^2$ value on test data than training data? | One explanation might relate to how you subset your test data (they way you split training and testing data). If your test data only consists of (just a few) similar observations then it is very likel | Higher $r^2$ value on test data than training data?
One explanation might relate to how you subset your test data (they way you split training and testing data). If your test data only consists of (just a few) similar observations then it is very likely for your R-squared measure to be different than that of the traini... | Higher $r^2$ value on test data than training data?
One explanation might relate to how you subset your test data (they way you split training and testing data). If your test data only consists of (just a few) similar observations then it is very likel |
49,429 | Find 1 dimensional sufficient statistic for $Beta(\alpha, 2\alpha)$ | Already answered in comments...
PDF of $X\sim\mathcal{Be}(\alpha,2\alpha)$ is
$$f(x;\alpha)=\frac{x^{\alpha-1}(1-x)^{2\alpha-1}}{B(\alpha,2\alpha)}\mathbf 1_{0<x<1},\quad\alpha>0$$
Suppose $(X_1,X_2,\cdots,X_n)$ is a random sample drawn from the above distribution.
Joint PDF of $(X_1,X_2,\cdots,X_n)$ is
\begin{align}f_... | Find 1 dimensional sufficient statistic for $Beta(\alpha, 2\alpha)$ | Already answered in comments...
PDF of $X\sim\mathcal{Be}(\alpha,2\alpha)$ is
$$f(x;\alpha)=\frac{x^{\alpha-1}(1-x)^{2\alpha-1}}{B(\alpha,2\alpha)}\mathbf 1_{0<x<1},\quad\alpha>0$$
Suppose $(X_1,X_2,\ | Find 1 dimensional sufficient statistic for $Beta(\alpha, 2\alpha)$
Already answered in comments...
PDF of $X\sim\mathcal{Be}(\alpha,2\alpha)$ is
$$f(x;\alpha)=\frac{x^{\alpha-1}(1-x)^{2\alpha-1}}{B(\alpha,2\alpha)}\mathbf 1_{0<x<1},\quad\alpha>0$$
Suppose $(X_1,X_2,\cdots,X_n)$ is a random sample drawn from the above ... | Find 1 dimensional sufficient statistic for $Beta(\alpha, 2\alpha)$
Already answered in comments...
PDF of $X\sim\mathcal{Be}(\alpha,2\alpha)$ is
$$f(x;\alpha)=\frac{x^{\alpha-1}(1-x)^{2\alpha-1}}{B(\alpha,2\alpha)}\mathbf 1_{0<x<1},\quad\alpha>0$$
Suppose $(X_1,X_2,\ |
49,430 | Closed form recurrence formula for getting N consecutive heads on a coin | Let $T^{(k)}$ be the time it takes to see the first run of $k$ successes.
Let $X\sim\mathrm{Ber}(p)$ be independent of $T^{(k)}$ for every $k$. Then,
$$
T^{(k)} = (T^{(k-1)}+1)\, X + (T^{(k-1)}+1+T^{(k)}) \, (1 - X) \, ,
$$
because, in words, if I see a success in the current trial, then the time to get $k$ consecut... | Closed form recurrence formula for getting N consecutive heads on a coin | Let $T^{(k)}$ be the time it takes to see the first run of $k$ successes.
Let $X\sim\mathrm{Ber}(p)$ be independent of $T^{(k)}$ for every $k$. Then,
$$
T^{(k)} = (T^{(k-1)}+1)\, X + (T^{(k-1)}+1+T | Closed form recurrence formula for getting N consecutive heads on a coin
Let $T^{(k)}$ be the time it takes to see the first run of $k$ successes.
Let $X\sim\mathrm{Ber}(p)$ be independent of $T^{(k)}$ for every $k$. Then,
$$
T^{(k)} = (T^{(k-1)}+1)\, X + (T^{(k-1)}+1+T^{(k)}) \, (1 - X) \, ,
$$
because, in words, i... | Closed form recurrence formula for getting N consecutive heads on a coin
Let $T^{(k)}$ be the time it takes to see the first run of $k$ successes.
Let $X\sim\mathrm{Ber}(p)$ be independent of $T^{(k)}$ for every $k$. Then,
$$
T^{(k)} = (T^{(k-1)}+1)\, X + (T^{(k-1)}+1+T |
49,431 | Forecasting high frequency variable with low frequency predictor | There are two quick and dirty solutions. First would be to disaggregate series B to weekly values (R package tempdisagg is great for that) and then do a usual model. Second aggregate series A to monthly frequency, do a forecast and then use disaggregation on the forecast.
The more theoretical approach would be casting... | Forecasting high frequency variable with low frequency predictor | There are two quick and dirty solutions. First would be to disaggregate series B to weekly values (R package tempdisagg is great for that) and then do a usual model. Second aggregate series A to month | Forecasting high frequency variable with low frequency predictor
There are two quick and dirty solutions. First would be to disaggregate series B to weekly values (R package tempdisagg is great for that) and then do a usual model. Second aggregate series A to monthly frequency, do a forecast and then use disaggregation... | Forecasting high frequency variable with low frequency predictor
There are two quick and dirty solutions. First would be to disaggregate series B to weekly values (R package tempdisagg is great for that) and then do a usual model. Second aggregate series A to month |
49,432 | How to calculate F-Measure from Precision Recall Curve | Precision-Recall curve and ROC curve (doesn't matter they are just the mirror images of each other) are used to give you the sense of the quality of the binary classifier for the different values for some parameter that affects the performance of your classifier. Now, F1 are particular scores which combine both precisi... | How to calculate F-Measure from Precision Recall Curve | Precision-Recall curve and ROC curve (doesn't matter they are just the mirror images of each other) are used to give you the sense of the quality of the binary classifier for the different values for | How to calculate F-Measure from Precision Recall Curve
Precision-Recall curve and ROC curve (doesn't matter they are just the mirror images of each other) are used to give you the sense of the quality of the binary classifier for the different values for some parameter that affects the performance of your classifier. N... | How to calculate F-Measure from Precision Recall Curve
Precision-Recall curve and ROC curve (doesn't matter they are just the mirror images of each other) are used to give you the sense of the quality of the binary classifier for the different values for |
49,433 | How to calculate F-Measure from Precision Recall Curve | It is hard to read off F1 (or any other weighted F-measure) directly from a Precision-Recall graph, because of needing to work with reciprocals (harmonic mean).
But if instead you plot the reciprocal Precision & Recall, then values of the F-measure form isobars (straight lines with equal values) with gradient depending... | How to calculate F-Measure from Precision Recall Curve | It is hard to read off F1 (or any other weighted F-measure) directly from a Precision-Recall graph, because of needing to work with reciprocals (harmonic mean).
But if instead you plot the reciprocal | How to calculate F-Measure from Precision Recall Curve
It is hard to read off F1 (or any other weighted F-measure) directly from a Precision-Recall graph, because of needing to work with reciprocals (harmonic mean).
But if instead you plot the reciprocal Precision & Recall, then values of the F-measure form isobars (st... | How to calculate F-Measure from Precision Recall Curve
It is hard to read off F1 (or any other weighted F-measure) directly from a Precision-Recall graph, because of needing to work with reciprocals (harmonic mean).
But if instead you plot the reciprocal |
49,434 | Determining if a function is additive | Although the second order mixed differences
$$D_{12}f(x_1, x_2, y_1, y_2) = f(x_1,y_1)+f(x_2,y_2)-f(x_1,y_2)-f(x_2,y_1)$$
provide useful information, by themselves they do not have enough power to discriminate between slightly noisy additive functions and non-additive functions.
The trick is to compare $D_{12},$ suitab... | Determining if a function is additive | Although the second order mixed differences
$$D_{12}f(x_1, x_2, y_1, y_2) = f(x_1,y_1)+f(x_2,y_2)-f(x_1,y_2)-f(x_2,y_1)$$
provide useful information, by themselves they do not have enough power to dis | Determining if a function is additive
Although the second order mixed differences
$$D_{12}f(x_1, x_2, y_1, y_2) = f(x_1,y_1)+f(x_2,y_2)-f(x_1,y_2)-f(x_2,y_1)$$
provide useful information, by themselves they do not have enough power to discriminate between slightly noisy additive functions and non-additive functions.
Th... | Determining if a function is additive
Although the second order mixed differences
$$D_{12}f(x_1, x_2, y_1, y_2) = f(x_1,y_1)+f(x_2,y_2)-f(x_1,y_2)-f(x_2,y_1)$$
provide useful information, by themselves they do not have enough power to dis |
49,435 | Choosing number of PCA components when multiple samples for each data point are available | I'm not sure that I completely understand the data. Do you have M replicates at N timepoints of a response in $\Re^d$? That would make $N \times M \times D$ actual numbers? Are the trajectories vector valued functions?
If I have understood this correctly, I don't think that your replicates are going to tell you anythin... | Choosing number of PCA components when multiple samples for each data point are available | I'm not sure that I completely understand the data. Do you have M replicates at N timepoints of a response in $\Re^d$? That would make $N \times M \times D$ actual numbers? Are the trajectories vector | Choosing number of PCA components when multiple samples for each data point are available
I'm not sure that I completely understand the data. Do you have M replicates at N timepoints of a response in $\Re^d$? That would make $N \times M \times D$ actual numbers? Are the trajectories vector valued functions?
If I have u... | Choosing number of PCA components when multiple samples for each data point are available
I'm not sure that I completely understand the data. Do you have M replicates at N timepoints of a response in $\Re^d$? That would make $N \times M \times D$ actual numbers? Are the trajectories vector |
49,436 | Choosing number of PCA components when multiple samples for each data point are available | In order to advance the discussion here I will describe two approaches that I am currently using.
For convenience I will repeat the notation here. There are $N$ points $\bar{\mathbf{x}}_i \in \mathbb{R}^D$, and each point is an average over $M$ repeated measurements $\bar{\mathbf{x}}_i=\frac{1}{M}\sum_{j=1}^M \mathbf{x... | Choosing number of PCA components when multiple samples for each data point are available | In order to advance the discussion here I will describe two approaches that I am currently using.
For convenience I will repeat the notation here. There are $N$ points $\bar{\mathbf{x}}_i \in \mathbb{ | Choosing number of PCA components when multiple samples for each data point are available
In order to advance the discussion here I will describe two approaches that I am currently using.
For convenience I will repeat the notation here. There are $N$ points $\bar{\mathbf{x}}_i \in \mathbb{R}^D$, and each point is an av... | Choosing number of PCA components when multiple samples for each data point are available
In order to advance the discussion here I will describe two approaches that I am currently using.
For convenience I will repeat the notation here. There are $N$ points $\bar{\mathbf{x}}_i \in \mathbb{ |
49,437 | Notation for Random Bernoulli-Like Vector With Fixed Sum | It is immaterial that the $p_i$ sum to unity. The problem describes a sum of Bernoulli$(p_i)$ distributions and conditions on the sum equalling $c$, $0 \le c \le k$. (I believe that is as far as we will get in terms of finding names for this procedure.) It is of course a discrete distribution. The nonzero probabiliti... | Notation for Random Bernoulli-Like Vector With Fixed Sum | It is immaterial that the $p_i$ sum to unity. The problem describes a sum of Bernoulli$(p_i)$ distributions and conditions on the sum equalling $c$, $0 \le c \le k$. (I believe that is as far as we | Notation for Random Bernoulli-Like Vector With Fixed Sum
It is immaterial that the $p_i$ sum to unity. The problem describes a sum of Bernoulli$(p_i)$ distributions and conditions on the sum equalling $c$, $0 \le c \le k$. (I believe that is as far as we will get in terms of finding names for this procedure.) It is o... | Notation for Random Bernoulli-Like Vector With Fixed Sum
It is immaterial that the $p_i$ sum to unity. The problem describes a sum of Bernoulli$(p_i)$ distributions and conditions on the sum equalling $c$, $0 \le c \le k$. (I believe that is as far as we |
49,438 | Find the limiting distribution of $\sqrt{n} \left(\sqrt{\bar{X}} -1 \right) $ if $\sqrt{n} \left( \bar{X}-1 \right) \to N(0,1)$ | The result is correct (up to a factor of $\sigma$, which is an unimportant typographical omission). This answer provides two separate ways to double-check it.
We can in fact obtain the PDF of the transformed variables directly: when the $X_n$ are exactly Normal (and not just asymptotically so), the PDF of $\sqrt{n}\le... | Find the limiting distribution of $\sqrt{n} \left(\sqrt{\bar{X}} -1 \right) $ if $\sqrt{n} \left( \b | The result is correct (up to a factor of $\sigma$, which is an unimportant typographical omission). This answer provides two separate ways to double-check it.
We can in fact obtain the PDF of the tra | Find the limiting distribution of $\sqrt{n} \left(\sqrt{\bar{X}} -1 \right) $ if $\sqrt{n} \left( \bar{X}-1 \right) \to N(0,1)$
The result is correct (up to a factor of $\sigma$, which is an unimportant typographical omission). This answer provides two separate ways to double-check it.
We can in fact obtain the PDF of... | Find the limiting distribution of $\sqrt{n} \left(\sqrt{\bar{X}} -1 \right) $ if $\sqrt{n} \left( \b
The result is correct (up to a factor of $\sigma$, which is an unimportant typographical omission). This answer provides two separate ways to double-check it.
We can in fact obtain the PDF of the tra |
49,439 | How to calculate the chance of getting completely unbalanced groups? (with R) | Taking the reworded question as a starting point:
A) What is the chance of getting all males in one group (treatment/control) while there are all females in the other for different sample sizes?
With n=1, the chance of picking a male from the population is 0.5.
With n=2, the chance that the first pick is a male is 0.5.... | How to calculate the chance of getting completely unbalanced groups? (with R) | Taking the reworded question as a starting point:
A) What is the chance of getting all males in one group (treatment/control) while there are all females in the other for different sample sizes?
With | How to calculate the chance of getting completely unbalanced groups? (with R)
Taking the reworded question as a starting point:
A) What is the chance of getting all males in one group (treatment/control) while there are all females in the other for different sample sizes?
With n=1, the chance of picking a male from the... | How to calculate the chance of getting completely unbalanced groups? (with R)
Taking the reworded question as a starting point:
A) What is the chance of getting all males in one group (treatment/control) while there are all females in the other for different sample sizes?
With |
49,440 | Law of Large numbers and central limit theorem | There are two basic criteria used to apply the strong law of large numbers. One of them requires that the variables are IID and that their expectation be finite. Since $X_1, ..., X_n$ are IID, so are $X_1^2, ..., X_n^2$, and $E(X_i^2) = 5$ is finite. So
$$ \frac{\sum_{i=1}^nX_i^2}{n}$$
converges almost surely to the ex... | Law of Large numbers and central limit theorem | There are two basic criteria used to apply the strong law of large numbers. One of them requires that the variables are IID and that their expectation be finite. Since $X_1, ..., X_n$ are IID, so are | Law of Large numbers and central limit theorem
There are two basic criteria used to apply the strong law of large numbers. One of them requires that the variables are IID and that their expectation be finite. Since $X_1, ..., X_n$ are IID, so are $X_1^2, ..., X_n^2$, and $E(X_i^2) = 5$ is finite. So
$$ \frac{\sum_{i=1}... | Law of Large numbers and central limit theorem
There are two basic criteria used to apply the strong law of large numbers. One of them requires that the variables are IID and that their expectation be finite. Since $X_1, ..., X_n$ are IID, so are |
49,441 | How can I estimate theta for the inverse hyperbolic sine transformation? | After a couple more days of thinking about the problem, I have two tentative answers.
Select theta so that the transformed data is close to normal as measured by goodness of fit. For example choose theta to maximize the p-value of the Shapiro-Wik test.
set.seed(1)
x <- rnorm(1000)
xt <- Inv.IHS(x, theta=2)
Shapiro.t... | How can I estimate theta for the inverse hyperbolic sine transformation? | After a couple more days of thinking about the problem, I have two tentative answers.
Select theta so that the transformed data is close to normal as measured by goodness of fit. For example choose | How can I estimate theta for the inverse hyperbolic sine transformation?
After a couple more days of thinking about the problem, I have two tentative answers.
Select theta so that the transformed data is close to normal as measured by goodness of fit. For example choose theta to maximize the p-value of the Shapiro-Wi... | How can I estimate theta for the inverse hyperbolic sine transformation?
After a couple more days of thinking about the problem, I have two tentative answers.
Select theta so that the transformed data is close to normal as measured by goodness of fit. For example choose |
49,442 | Pairwise comparisons for a regression with sandwich estimates (in R) | One solution is actually given as an example in the book on the multcomp package, section 4.6:
Bretz, F., Hothorn, T., & Westfall, P. H. (2011). Multiple comparisons using R. Boca Raton, FL: CRC Press.
One only needs to slightly adapt your code (everything needs to be in one data.frame instead of floating around):
requ... | Pairwise comparisons for a regression with sandwich estimates (in R) | One solution is actually given as an example in the book on the multcomp package, section 4.6:
Bretz, F., Hothorn, T., & Westfall, P. H. (2011). Multiple comparisons using R. Boca Raton, FL: CRC Press | Pairwise comparisons for a regression with sandwich estimates (in R)
One solution is actually given as an example in the book on the multcomp package, section 4.6:
Bretz, F., Hothorn, T., & Westfall, P. H. (2011). Multiple comparisons using R. Boca Raton, FL: CRC Press.
One only needs to slightly adapt your code (every... | Pairwise comparisons for a regression with sandwich estimates (in R)
One solution is actually given as an example in the book on the multcomp package, section 4.6:
Bretz, F., Hothorn, T., & Westfall, P. H. (2011). Multiple comparisons using R. Boca Raton, FL: CRC Press |
49,443 | Goodness of fit test for exponential distribution | Firstly, Rearrange the table so it makes better sense: and calculate mean via calculator.calculator. In order to do this take the midpoint of each interval for time and enter this on the display alongside the frequencies. you should get 40. this is the MEAN however, so to get lambda, use the following formula lambda = ... | Goodness of fit test for exponential distribution | Firstly, Rearrange the table so it makes better sense: and calculate mean via calculator.calculator. In order to do this take the midpoint of each interval for time and enter this on the display along | Goodness of fit test for exponential distribution
Firstly, Rearrange the table so it makes better sense: and calculate mean via calculator.calculator. In order to do this take the midpoint of each interval for time and enter this on the display alongside the frequencies. you should get 40. this is the MEAN however, so ... | Goodness of fit test for exponential distribution
Firstly, Rearrange the table so it makes better sense: and calculate mean via calculator.calculator. In order to do this take the midpoint of each interval for time and enter this on the display along |
49,444 | GLMM model specification help gender effects + an effect that is nested only within female | You have three groups: males (M), females with young (FY), females without young (FN). Although I understand why it would be conceptually tempting to think of this as two factors (gender and parental status), I think from a statistical point of view it is better to think of these groups as comprising a single factor, "... | GLMM model specification help gender effects + an effect that is nested only within female | You have three groups: males (M), females with young (FY), females without young (FN). Although I understand why it would be conceptually tempting to think of this as two factors (gender and parental | GLMM model specification help gender effects + an effect that is nested only within female
You have three groups: males (M), females with young (FY), females without young (FN). Although I understand why it would be conceptually tempting to think of this as two factors (gender and parental status), I think from a stati... | GLMM model specification help gender effects + an effect that is nested only within female
You have three groups: males (M), females with young (FY), females without young (FN). Although I understand why it would be conceptually tempting to think of this as two factors (gender and parental |
49,445 | How to evaluate/validate clusters using multiple clustering methods | As daniellopez46 noted, I think you are thinking of consensus clustering where you basically form an ensemble of different clustering runs. What is a bit strange here is that you would want the ensemble to contain results from different clustering methods which can be very misleading. I say this because unlike supervis... | How to evaluate/validate clusters using multiple clustering methods | As daniellopez46 noted, I think you are thinking of consensus clustering where you basically form an ensemble of different clustering runs. What is a bit strange here is that you would want the ensemb | How to evaluate/validate clusters using multiple clustering methods
As daniellopez46 noted, I think you are thinking of consensus clustering where you basically form an ensemble of different clustering runs. What is a bit strange here is that you would want the ensemble to contain results from different clustering meth... | How to evaluate/validate clusters using multiple clustering methods
As daniellopez46 noted, I think you are thinking of consensus clustering where you basically form an ensemble of different clustering runs. What is a bit strange here is that you would want the ensemb |
49,446 | How to compute the maximum a posteriori probability (MAP) estimate with / without a prior | As mentioned in a comment, the MAP estimate is the maximum likelihood estimate when you omit $g(\theta)$ or if it is a constant. If $g(\theta)$ is not a constant, then there are of course various methods for finding the MAP estimate. Omitting the survey sampling aspect (or assuming we have a completely representative s... | How to compute the maximum a posteriori probability (MAP) estimate with / without a prior | As mentioned in a comment, the MAP estimate is the maximum likelihood estimate when you omit $g(\theta)$ or if it is a constant. If $g(\theta)$ is not a constant, then there are of course various meth | How to compute the maximum a posteriori probability (MAP) estimate with / without a prior
As mentioned in a comment, the MAP estimate is the maximum likelihood estimate when you omit $g(\theta)$ or if it is a constant. If $g(\theta)$ is not a constant, then there are of course various methods for finding the MAP estima... | How to compute the maximum a posteriori probability (MAP) estimate with / without a prior
As mentioned in a comment, the MAP estimate is the maximum likelihood estimate when you omit $g(\theta)$ or if it is a constant. If $g(\theta)$ is not a constant, then there are of course various meth |
49,447 | How to compute the maximum a posteriori probability (MAP) estimate with / without a prior | Calculate the the chances of A and B from the samples you have using Maximum Likelihood. Multiply the chances you get for A by 0.4 and B by 0.6. Compare the results. The MAP Estimator is the higher one.
How do you interpret no knowledge?
If you say it means any case has the same chances, use the above and multiply by 0... | How to compute the maximum a posteriori probability (MAP) estimate with / without a prior | Calculate the the chances of A and B from the samples you have using Maximum Likelihood. Multiply the chances you get for A by 0.4 and B by 0.6. Compare the results. The MAP Estimator is the higher on | How to compute the maximum a posteriori probability (MAP) estimate with / without a prior
Calculate the the chances of A and B from the samples you have using Maximum Likelihood. Multiply the chances you get for A by 0.4 and B by 0.6. Compare the results. The MAP Estimator is the higher one.
How do you interpret no kno... | How to compute the maximum a posteriori probability (MAP) estimate with / without a prior
Calculate the the chances of A and B from the samples you have using Maximum Likelihood. Multiply the chances you get for A by 0.4 and B by 0.6. Compare the results. The MAP Estimator is the higher on |
49,448 | Construct confidence interval of the mean for auto-correlated data | Here are a couple thoughts that may be helpful:
Auto-correlation doesn't matter when you only look at a single t at a time. So, at a fixed time t, you could just run a t-test to check for a difference in means. If you run the t-test for each time separately, then you get a bunch of p-values. Because of auto-correlatio... | Construct confidence interval of the mean for auto-correlated data | Here are a couple thoughts that may be helpful:
Auto-correlation doesn't matter when you only look at a single t at a time. So, at a fixed time t, you could just run a t-test to check for a differenc | Construct confidence interval of the mean for auto-correlated data
Here are a couple thoughts that may be helpful:
Auto-correlation doesn't matter when you only look at a single t at a time. So, at a fixed time t, you could just run a t-test to check for a difference in means. If you run the t-test for each time separ... | Construct confidence interval of the mean for auto-correlated data
Here are a couple thoughts that may be helpful:
Auto-correlation doesn't matter when you only look at a single t at a time. So, at a fixed time t, you could just run a t-test to check for a differenc |
49,449 | Dealing with missing data in the prediction set only | (I'll let someone else address the estimation of the missing data. You may want to directly model the probability that the observation is each level of the unknown factor using knowledge of other covariate values, and possibly outside information, e.g., priors etc. There are strategies such as propensity scores that ... | Dealing with missing data in the prediction set only | (I'll let someone else address the estimation of the missing data. You may want to directly model the probability that the observation is each level of the unknown factor using knowledge of other cov | Dealing with missing data in the prediction set only
(I'll let someone else address the estimation of the missing data. You may want to directly model the probability that the observation is each level of the unknown factor using knowledge of other covariate values, and possibly outside information, e.g., priors etc. ... | Dealing with missing data in the prediction set only
(I'll let someone else address the estimation of the missing data. You may want to directly model the probability that the observation is each level of the unknown factor using knowledge of other cov |
49,450 | Fitting a Generalized Linear Model (GLM) in R | There are three components to the GLM: an outcome variable, a linear predictor and a link function. The link function in the GLM relates the expected value of the outcome variable to the linear predictor. In other words, not the expected value itself, but a function of it is modeled by the linear predictor. An example ... | Fitting a Generalized Linear Model (GLM) in R | There are three components to the GLM: an outcome variable, a linear predictor and a link function. The link function in the GLM relates the expected value of the outcome variable to the linear predic | Fitting a Generalized Linear Model (GLM) in R
There are three components to the GLM: an outcome variable, a linear predictor and a link function. The link function in the GLM relates the expected value of the outcome variable to the linear predictor. In other words, not the expected value itself, but a function of it i... | Fitting a Generalized Linear Model (GLM) in R
There are three components to the GLM: an outcome variable, a linear predictor and a link function. The link function in the GLM relates the expected value of the outcome variable to the linear predic |
49,451 | Testing for significance between means, having one normal distributed sample and one non normal distributed | If you are 100% sure that the two samples are drawn from populations with different distributions (one Gaussian, one not), are you sure you need any statistical test? You are already sure that the two populations are different. Isn't that enough? Does it really help to test for differences in means or medians? (The ans... | Testing for significance between means, having one normal distributed sample and one non normal dist | If you are 100% sure that the two samples are drawn from populations with different distributions (one Gaussian, one not), are you sure you need any statistical test? You are already sure that the two | Testing for significance between means, having one normal distributed sample and one non normal distributed
If you are 100% sure that the two samples are drawn from populations with different distributions (one Gaussian, one not), are you sure you need any statistical test? You are already sure that the two populations... | Testing for significance between means, having one normal distributed sample and one non normal dist
If you are 100% sure that the two samples are drawn from populations with different distributions (one Gaussian, one not), are you sure you need any statistical test? You are already sure that the two |
49,452 | Testing for significance between means, having one normal distributed sample and one non normal distributed | You probably want the The Wilcoxon Rank Sum, also called Mann-Whitney U here. The Signed Rank Test is for paired samples, so not appropriate in your case.
Your choice is basically between a t-test or Wilcoxon, and come down to a balance between
On the one hand, t-test is more powerful particularly in small samples (w... | Testing for significance between means, having one normal distributed sample and one non normal dist | You probably want the The Wilcoxon Rank Sum, also called Mann-Whitney U here. The Signed Rank Test is for paired samples, so not appropriate in your case.
Your choice is basically between a t-test or | Testing for significance between means, having one normal distributed sample and one non normal distributed
You probably want the The Wilcoxon Rank Sum, also called Mann-Whitney U here. The Signed Rank Test is for paired samples, so not appropriate in your case.
Your choice is basically between a t-test or Wilcoxon, a... | Testing for significance between means, having one normal distributed sample and one non normal dist
You probably want the The Wilcoxon Rank Sum, also called Mann-Whitney U here. The Signed Rank Test is for paired samples, so not appropriate in your case.
Your choice is basically between a t-test or |
49,453 | Testing for significance between means, having one normal distributed sample and one non normal distributed | If data in the treatment group is not normal while the control group is it sounds like the treatment may only be affecting a subset of the sample or having variable levels of effect. Comparing means under such circumstances would be losing out on this information. You should attempt to offer explanations for why this c... | Testing for significance between means, having one normal distributed sample and one non normal dist | If data in the treatment group is not normal while the control group is it sounds like the treatment may only be affecting a subset of the sample or having variable levels of effect. Comparing means u | Testing for significance between means, having one normal distributed sample and one non normal distributed
If data in the treatment group is not normal while the control group is it sounds like the treatment may only be affecting a subset of the sample or having variable levels of effect. Comparing means under such ci... | Testing for significance between means, having one normal distributed sample and one non normal dist
If data in the treatment group is not normal while the control group is it sounds like the treatment may only be affecting a subset of the sample or having variable levels of effect. Comparing means u |
49,454 | Testing for significance between means, having one normal distributed sample and one non normal distributed | I think in such case when we have two samples one normally distributed and another was not it is more accurate to make transformation for data to get normal distribution for two samples and compare the means by t-test. As t-test is more powerful for small samples. Otherwise we can used non parametric test depend on the... | Testing for significance between means, having one normal distributed sample and one non normal dist | I think in such case when we have two samples one normally distributed and another was not it is more accurate to make transformation for data to get normal distribution for two samples and compare th | Testing for significance between means, having one normal distributed sample and one non normal distributed
I think in such case when we have two samples one normally distributed and another was not it is more accurate to make transformation for data to get normal distribution for two samples and compare the means by t... | Testing for significance between means, having one normal distributed sample and one non normal dist
I think in such case when we have two samples one normally distributed and another was not it is more accurate to make transformation for data to get normal distribution for two samples and compare th |
49,455 | Correct definition of number of parameters $K$ in Akaike Information Criterion | This is how the original 1974 paper by Hirotugu Akaike defines the AIC:
AIC = (-2)log(maximum likelihood) + 2(number of independently adjusted parameters within the model)
The error term is not a parameter which you're independently trying to adjust, but the intercept is (e.g. your slope might be zero and the data be... | Correct definition of number of parameters $K$ in Akaike Information Criterion | This is how the original 1974 paper by Hirotugu Akaike defines the AIC:
AIC = (-2)log(maximum likelihood) + 2(number of independently adjusted parameters within the model)
The error term is not a pa | Correct definition of number of parameters $K$ in Akaike Information Criterion
This is how the original 1974 paper by Hirotugu Akaike defines the AIC:
AIC = (-2)log(maximum likelihood) + 2(number of independently adjusted parameters within the model)
The error term is not a parameter which you're independently trying... | Correct definition of number of parameters $K$ in Akaike Information Criterion
This is how the original 1974 paper by Hirotugu Akaike defines the AIC:
AIC = (-2)log(maximum likelihood) + 2(number of independently adjusted parameters within the model)
The error term is not a pa |
49,456 | Interpreting Two-way repeated measures ANOVA results: Post-hoc tests allowed without significant interaction? | Well, this seems like an old problem, but I'm still tempted to share my two cents.
I am tempted to included post-hoc comparisons to compare treatment levels at each time interval to show that the difference between groups disappears on day 11
I don't think you should do this. After all, ANOVA said that the interactio... | Interpreting Two-way repeated measures ANOVA results: Post-hoc tests allowed without significant int | Well, this seems like an old problem, but I'm still tempted to share my two cents.
I am tempted to included post-hoc comparisons to compare treatment levels at each time interval to show that the dif | Interpreting Two-way repeated measures ANOVA results: Post-hoc tests allowed without significant interaction?
Well, this seems like an old problem, but I'm still tempted to share my two cents.
I am tempted to included post-hoc comparisons to compare treatment levels at each time interval to show that the difference be... | Interpreting Two-way repeated measures ANOVA results: Post-hoc tests allowed without significant int
Well, this seems like an old problem, but I'm still tempted to share my two cents.
I am tempted to included post-hoc comparisons to compare treatment levels at each time interval to show that the dif |
49,457 | Testing for Poisson process | If you like Python / numpy / matplotlib, here is a small example demonstrating Remark 6.3:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import scipy.stats
# interval between two events is distributed as an exponential
>>> delta_t = scipy.stats.expon.rvs(size=10000)
>>> t = np.cumsum(delta_t)
>>> plt... | Testing for Poisson process | If you like Python / numpy / matplotlib, here is a small example demonstrating Remark 6.3:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import scipy.stats
# interval between two ev | Testing for Poisson process
If you like Python / numpy / matplotlib, here is a small example demonstrating Remark 6.3:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import scipy.stats
# interval between two events is distributed as an exponential
>>> delta_t = scipy.stats.expon.rvs(size=10000)
>>> t ... | Testing for Poisson process
If you like Python / numpy / matplotlib, here is a small example demonstrating Remark 6.3:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import scipy.stats
# interval between two ev |
49,458 | Experiment design question | The real question is: do you have a hypothesis? In Milgram's study, the real important information is really descriptive (e.g., how many people made it to 450 volts) rather than hypothesis driven. So, in short, the answer is no, you don't always need a statistical hypothesis beforehand. Note that many people may say th... | Experiment design question | The real question is: do you have a hypothesis? In Milgram's study, the real important information is really descriptive (e.g., how many people made it to 450 volts) rather than hypothesis driven. So, | Experiment design question
The real question is: do you have a hypothesis? In Milgram's study, the real important information is really descriptive (e.g., how many people made it to 450 volts) rather than hypothesis driven. So, in short, the answer is no, you don't always need a statistical hypothesis beforehand. Note ... | Experiment design question
The real question is: do you have a hypothesis? In Milgram's study, the real important information is really descriptive (e.g., how many people made it to 450 volts) rather than hypothesis driven. So, |
49,459 | Resources for matrix calculus for optimization | I think a better book is Matrix Calculus by the same Jan Magnus (with H. Neudecker). It goes a little deeper into theory than the Matrix Algebra which is essentially a set of exercises (very good ones, but still... little room for the proofs and discussion of where the math stuff applies.) I heard that the first editio... | Resources for matrix calculus for optimization | I think a better book is Matrix Calculus by the same Jan Magnus (with H. Neudecker). It goes a little deeper into theory than the Matrix Algebra which is essentially a set of exercises (very good ones | Resources for matrix calculus for optimization
I think a better book is Matrix Calculus by the same Jan Magnus (with H. Neudecker). It goes a little deeper into theory than the Matrix Algebra which is essentially a set of exercises (very good ones, but still... little room for the proofs and discussion of where the mat... | Resources for matrix calculus for optimization
I think a better book is Matrix Calculus by the same Jan Magnus (with H. Neudecker). It goes a little deeper into theory than the Matrix Algebra which is essentially a set of exercises (very good ones |
49,460 | Resources for matrix calculus for optimization | A good paper to read is Dwyer (1967) "Some Applications of Matrix Derivatives in Multivariate Analysis". It covers matrix calculus from the perspective of statistical applications. In particular it contains two tables of identities which makes it a useful reference to have on hand.
The paper is available behind a pay w... | Resources for matrix calculus for optimization | A good paper to read is Dwyer (1967) "Some Applications of Matrix Derivatives in Multivariate Analysis". It covers matrix calculus from the perspective of statistical applications. In particular it co | Resources for matrix calculus for optimization
A good paper to read is Dwyer (1967) "Some Applications of Matrix Derivatives in Multivariate Analysis". It covers matrix calculus from the perspective of statistical applications. In particular it contains two tables of identities which makes it a useful reference to have... | Resources for matrix calculus for optimization
A good paper to read is Dwyer (1967) "Some Applications of Matrix Derivatives in Multivariate Analysis". It covers matrix calculus from the perspective of statistical applications. In particular it co |
49,461 | Regression with correlated explanatory variables | In the example you give it would make no sense to talk about only two of the explanatory variables ($x_1$, $x_2$) influencing a dependent variable ($y$), as the third ($x_3$) is derived from them. For e.g. the linear model with interactions, the fitted value of $y$ is given by
$$\hat{y} = \beta_0 + \beta_1 x_1 + \beta_... | Regression with correlated explanatory variables | In the example you give it would make no sense to talk about only two of the explanatory variables ($x_1$, $x_2$) influencing a dependent variable ($y$), as the third ($x_3$) is derived from them. For | Regression with correlated explanatory variables
In the example you give it would make no sense to talk about only two of the explanatory variables ($x_1$, $x_2$) influencing a dependent variable ($y$), as the third ($x_3$) is derived from them. For e.g. the linear model with interactions, the fitted value of $y$ is gi... | Regression with correlated explanatory variables
In the example you give it would make no sense to talk about only two of the explanatory variables ($x_1$, $x_2$) influencing a dependent variable ($y$), as the third ($x_3$) is derived from them. For |
49,462 | Regression with correlated explanatory variables | If you have some knowledge of a hypothetical relation (e.g., from the literature), then you might be interested in looking at non-linear as well as linear regression models. Standing on the shoulders of those who have worked on this stuff before you can give you some great vision.
If you are content to limit yourself ... | Regression with correlated explanatory variables | If you have some knowledge of a hypothetical relation (e.g., from the literature), then you might be interested in looking at non-linear as well as linear regression models. Standing on the shoulders | Regression with correlated explanatory variables
If you have some knowledge of a hypothetical relation (e.g., from the literature), then you might be interested in looking at non-linear as well as linear regression models. Standing on the shoulders of those who have worked on this stuff before you can give you some gr... | Regression with correlated explanatory variables
If you have some knowledge of a hypothetical relation (e.g., from the literature), then you might be interested in looking at non-linear as well as linear regression models. Standing on the shoulders |
49,463 | Pooled logistic regression with irregular intervals | You can model the time trend in the log odds of the probability that $Y=1$ using a spline function (and using other methods). I do not believe that the time points need to be equally spaced, but rather that they be discrete and have roughly the same schedule for every subject. | Pooled logistic regression with irregular intervals | You can model the time trend in the log odds of the probability that $Y=1$ using a spline function (and using other methods). I do not believe that the time points need to be equally spaced, but rath | Pooled logistic regression with irregular intervals
You can model the time trend in the log odds of the probability that $Y=1$ using a spline function (and using other methods). I do not believe that the time points need to be equally spaced, but rather that they be discrete and have roughly the same schedule for ever... | Pooled logistic regression with irregular intervals
You can model the time trend in the log odds of the probability that $Y=1$ using a spline function (and using other methods). I do not believe that the time points need to be equally spaced, but rath |
49,464 | Comparing multiple incidence rates | If you only have the four data points, I think the best way to do this is with a G^2 test. You want to start by assuming the frequency is a binomial distribution (every person in the population has the condition with probability p). And your null hypothesis is that p_1=p_2=p_3=p_4.
So the overall mean is (1800+539+490... | Comparing multiple incidence rates | If you only have the four data points, I think the best way to do this is with a G^2 test. You want to start by assuming the frequency is a binomial distribution (every person in the population has th | Comparing multiple incidence rates
If you only have the four data points, I think the best way to do this is with a G^2 test. You want to start by assuming the frequency is a binomial distribution (every person in the population has the condition with probability p). And your null hypothesis is that p_1=p_2=p_3=p_4.
S... | Comparing multiple incidence rates
If you only have the four data points, I think the best way to do this is with a G^2 test. You want to start by assuming the frequency is a binomial distribution (every person in the population has th |
49,465 | Comparing multiple incidence rates | Given the limited data you have to work with you may only be able to address this question by incorporating additional assumptions (or data?) regarding the process behind these incidence rates, then doing some manual modeling. Any statistical technique you use will be implicitly making such assumptions for you under t... | Comparing multiple incidence rates | Given the limited data you have to work with you may only be able to address this question by incorporating additional assumptions (or data?) regarding the process behind these incidence rates, then d | Comparing multiple incidence rates
Given the limited data you have to work with you may only be able to address this question by incorporating additional assumptions (or data?) regarding the process behind these incidence rates, then doing some manual modeling. Any statistical technique you use will be implicitly maki... | Comparing multiple incidence rates
Given the limited data you have to work with you may only be able to address this question by incorporating additional assumptions (or data?) regarding the process behind these incidence rates, then d |
49,466 | Hausman test - wrong conclusion | Statistical significance doesn't mean the model is good. In fact, in this case, it's probably a sign that it's bad.
If your model is misspecified your estimate of the model variance could be wrong. Statistical significance depends on that estimate. If that estimate is wrong, you will get erroneous t-statistics and ther... | Hausman test - wrong conclusion | Statistical significance doesn't mean the model is good. In fact, in this case, it's probably a sign that it's bad.
If your model is misspecified your estimate of the model variance could be wrong. St | Hausman test - wrong conclusion
Statistical significance doesn't mean the model is good. In fact, in this case, it's probably a sign that it's bad.
If your model is misspecified your estimate of the model variance could be wrong. Statistical significance depends on that estimate. If that estimate is wrong, you will get... | Hausman test - wrong conclusion
Statistical significance doesn't mean the model is good. In fact, in this case, it's probably a sign that it's bad.
If your model is misspecified your estimate of the model variance could be wrong. St |
49,467 | Hausman test - wrong conclusion | You should definitely utilize the result from the Hausman test. Remember what the test does: it compares a consistent but less efficient estimator (fixed effects) to a more efficient estimator that is only consistent under the null (random effects),
$$H = (\beta_{FE}-\beta_{RE})'[Var(\beta_{FE})-Var(\beta_{RE})]^{-1}(... | Hausman test - wrong conclusion | You should definitely utilize the result from the Hausman test. Remember what the test does: it compares a consistent but less efficient estimator (fixed effects) to a more efficient estimator that is | Hausman test - wrong conclusion
You should definitely utilize the result from the Hausman test. Remember what the test does: it compares a consistent but less efficient estimator (fixed effects) to a more efficient estimator that is only consistent under the null (random effects),
$$H = (\beta_{FE}-\beta_{RE})'[Var(\b... | Hausman test - wrong conclusion
You should definitely utilize the result from the Hausman test. Remember what the test does: it compares a consistent but less efficient estimator (fixed effects) to a more efficient estimator that is |
49,468 | Validating correctness of ranking algorithm | If I understand correctly, what you would like to have is a measure to compare the underlying true ranking $\pi$ and the predicted ranking (i.e., the tournament ranking, or the simulated ranking) $\sigma$, where $\sigma$ is a function of some input parameters.
In the statistics literature, there are a number of distanc... | Validating correctness of ranking algorithm | If I understand correctly, what you would like to have is a measure to compare the underlying true ranking $\pi$ and the predicted ranking (i.e., the tournament ranking, or the simulated ranking) $\si | Validating correctness of ranking algorithm
If I understand correctly, what you would like to have is a measure to compare the underlying true ranking $\pi$ and the predicted ranking (i.e., the tournament ranking, or the simulated ranking) $\sigma$, where $\sigma$ is a function of some input parameters.
In the statisti... | Validating correctness of ranking algorithm
If I understand correctly, what you would like to have is a measure to compare the underlying true ranking $\pi$ and the predicted ranking (i.e., the tournament ranking, or the simulated ranking) $\si |
49,469 | Validate cluster analysis in R | This is definitely not a question for this site. I flagged it to be migrated to c-v. What do you mean by "do I have to use all these methods"? because in your code you added all the clustering techniques: k-means, hierarchical, self organizing maps. But at the beginning of the question you said you wanted to perform hi... | Validate cluster analysis in R | This is definitely not a question for this site. I flagged it to be migrated to c-v. What do you mean by "do I have to use all these methods"? because in your code you added all the clustering techniq | Validate cluster analysis in R
This is definitely not a question for this site. I flagged it to be migrated to c-v. What do you mean by "do I have to use all these methods"? because in your code you added all the clustering techniques: k-means, hierarchical, self organizing maps. But at the beginning of the question yo... | Validate cluster analysis in R
This is definitely not a question for this site. I flagged it to be migrated to c-v. What do you mean by "do I have to use all these methods"? because in your code you added all the clustering techniq |
49,470 | Validate cluster analysis in R | Did you try to get any of the warnings? warnings()
Most of the times multiple warnings in the correlation matrix generation are because of the NA cells than because of 0 cells. Check if any of the two columns to be correlated have absolutely no variation so that the correlation coefficient can not be generated.
Anothe... | Validate cluster analysis in R | Did you try to get any of the warnings? warnings()
Most of the times multiple warnings in the correlation matrix generation are because of the NA cells than because of 0 cells. Check if any of the two | Validate cluster analysis in R
Did you try to get any of the warnings? warnings()
Most of the times multiple warnings in the correlation matrix generation are because of the NA cells than because of 0 cells. Check if any of the two columns to be correlated have absolutely no variation so that the correlation coefficien... | Validate cluster analysis in R
Did you try to get any of the warnings? warnings()
Most of the times multiple warnings in the correlation matrix generation are because of the NA cells than because of 0 cells. Check if any of the two |
49,471 | Should I re-center variables when looking at moderator effect in men and women separately? | Centring: Centring does not change the significance of the r-square change of your interaction effect. It also will not change the values you get for a simple slopes analysis.
Thus, for most purposes it does not matter whether you centre or not. This applies both to the general analysis, and to the subgroup analysis.
... | Should I re-center variables when looking at moderator effect in men and women separately? | Centring: Centring does not change the significance of the r-square change of your interaction effect. It also will not change the values you get for a simple slopes analysis.
Thus, for most purposes | Should I re-center variables when looking at moderator effect in men and women separately?
Centring: Centring does not change the significance of the r-square change of your interaction effect. It also will not change the values you get for a simple slopes analysis.
Thus, for most purposes it does not matter whether yo... | Should I re-center variables when looking at moderator effect in men and women separately?
Centring: Centring does not change the significance of the r-square change of your interaction effect. It also will not change the values you get for a simple slopes analysis.
Thus, for most purposes |
49,472 | How to combine time-series based features with different frequencies | As suggested by @ChuckKillerDoll, you could find aggregate / derive features from your current measures, but chances are you will lose information by doing so. Another way to go about it, is to create three separate models and train a model for each of the frequency information matrices individually. These produce outp... | How to combine time-series based features with different frequencies | As suggested by @ChuckKillerDoll, you could find aggregate / derive features from your current measures, but chances are you will lose information by doing so. Another way to go about it, is to create | How to combine time-series based features with different frequencies
As suggested by @ChuckKillerDoll, you could find aggregate / derive features from your current measures, but chances are you will lose information by doing so. Another way to go about it, is to create three separate models and train a model for each o... | How to combine time-series based features with different frequencies
As suggested by @ChuckKillerDoll, you could find aggregate / derive features from your current measures, but chances are you will lose information by doing so. Another way to go about it, is to create |
49,473 | How to combine time-series based features with different frequencies | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
I would try an state-space model. The simplest possibl... | How to combine time-series based features with different frequencies | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| How to combine time-series based features with different frequencies
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | How to combine time-series based features with different frequencies
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
49,474 | How to combine time-series based features with different frequencies | IMHO, your problem is related to "feature engineering".
Dealing with financial market time series, I use to create one new column (feature) for each set of parameters of each indicator.
For example a simple moving average (SMA) is defined by the set = {LookbackPeriod,Frequency,ShortPeriod,LongPeriod} and each combinati... | How to combine time-series based features with different frequencies | IMHO, your problem is related to "feature engineering".
Dealing with financial market time series, I use to create one new column (feature) for each set of parameters of each indicator.
For example a | How to combine time-series based features with different frequencies
IMHO, your problem is related to "feature engineering".
Dealing with financial market time series, I use to create one new column (feature) for each set of parameters of each indicator.
For example a simple moving average (SMA) is defined by the set =... | How to combine time-series based features with different frequencies
IMHO, your problem is related to "feature engineering".
Dealing with financial market time series, I use to create one new column (feature) for each set of parameters of each indicator.
For example a |
49,475 | Sample size for multiple linear regression | Power analysis for multiple regression is quite complex as there are many moving parts and potentially several different tests of interest. The function pwr.f2.test is based on Cohen's book Statistical Power Analysis for the Behavioral Sciences and you can find detailed explanations and many examples there.
The most im... | Sample size for multiple linear regression | Power analysis for multiple regression is quite complex as there are many moving parts and potentially several different tests of interest. The function pwr.f2.test is based on Cohen's book Statistica | Sample size for multiple linear regression
Power analysis for multiple regression is quite complex as there are many moving parts and potentially several different tests of interest. The function pwr.f2.test is based on Cohen's book Statistical Power Analysis for the Behavioral Sciences and you can find detailed explan... | Sample size for multiple linear regression
Power analysis for multiple regression is quite complex as there are many moving parts and potentially several different tests of interest. The function pwr.f2.test is based on Cohen's book Statistica |
49,476 | Sample size for multiple linear regression | The best treatment of power for logistic regression I have seen was in Breslow and Day's volume 2 of "Statistical Methods In Cancer Research". The starting point is to realize that the simplest case is a binomial test and that the variance depends on N and the case proportion by way of var(t) = N*p*(1-p). Rather deligh... | Sample size for multiple linear regression | The best treatment of power for logistic regression I have seen was in Breslow and Day's volume 2 of "Statistical Methods In Cancer Research". The starting point is to realize that the simplest case i | Sample size for multiple linear regression
The best treatment of power for logistic regression I have seen was in Breslow and Day's volume 2 of "Statistical Methods In Cancer Research". The starting point is to realize that the simplest case is a binomial test and that the variance depends on N and the case proportion ... | Sample size for multiple linear regression
The best treatment of power for logistic regression I have seen was in Breslow and Day's volume 2 of "Statistical Methods In Cancer Research". The starting point is to realize that the simplest case i |
49,477 | What would be a better alternative to two balanced latin squares (human subject experiment)? | A classical Latin Square confounds two factor interactions with main effects. If the second Latin Square differs from the first in how it combines the levels, you may be able to break some of the confounding. If the two are identical but run in different orders, you'll have an estimate of pure error. Which is better... | What would be a better alternative to two balanced latin squares (human subject experiment)? | A classical Latin Square confounds two factor interactions with main effects. If the second Latin Square differs from the first in how it combines the levels, you may be able to break some of the con | What would be a better alternative to two balanced latin squares (human subject experiment)?
A classical Latin Square confounds two factor interactions with main effects. If the second Latin Square differs from the first in how it combines the levels, you may be able to break some of the confounding. If the two are i... | What would be a better alternative to two balanced latin squares (human subject experiment)?
A classical Latin Square confounds two factor interactions with main effects. If the second Latin Square differs from the first in how it combines the levels, you may be able to break some of the con |
49,478 | Is it invalid to use a single sample to estimate more than one proportion? | Is it fair for me to estimate the proportion of each colour and use the 'standard error of a proportion' for each colour independently?
Yes, absolutely. It's not just fair, but also correct.
For example, can I say that there are 30% white marbles with a margin error of $\sqrt{p(1-p)/n}$, and then do the same for bl... | Is it invalid to use a single sample to estimate more than one proportion? | Is it fair for me to estimate the proportion of each colour and use the 'standard error of a proportion' for each colour independently?
Yes, absolutely. It's not just fair, but also correct.
For e | Is it invalid to use a single sample to estimate more than one proportion?
Is it fair for me to estimate the proportion of each colour and use the 'standard error of a proportion' for each colour independently?
Yes, absolutely. It's not just fair, but also correct.
For example, can I say that there are 30% white ma... | Is it invalid to use a single sample to estimate more than one proportion?
Is it fair for me to estimate the proportion of each colour and use the 'standard error of a proportion' for each colour independently?
Yes, absolutely. It's not just fair, but also correct.
For e |
49,479 | Discrete Time Survival Analysis - Correct Way to Write Survival Function | The answer is that both are used, unfortunately. In the continuous case, you are right the distinction is unimportant. In the discrete case, the interpretation would be slightly different and therefore clarity is important.
In my experience, the most common definition of the survival function is $S(t) = Pr(T>t)$ and s... | Discrete Time Survival Analysis - Correct Way to Write Survival Function | The answer is that both are used, unfortunately. In the continuous case, you are right the distinction is unimportant. In the discrete case, the interpretation would be slightly different and therefor | Discrete Time Survival Analysis - Correct Way to Write Survival Function
The answer is that both are used, unfortunately. In the continuous case, you are right the distinction is unimportant. In the discrete case, the interpretation would be slightly different and therefore clarity is important.
In my experience, the ... | Discrete Time Survival Analysis - Correct Way to Write Survival Function
The answer is that both are used, unfortunately. In the continuous case, you are right the distinction is unimportant. In the discrete case, the interpretation would be slightly different and therefor |
49,480 | Use Random Forest model to make predictions from sensor data | Linear regressions are great because you can implement prediction very simply in any program that can multiply and add.
Random forests, on the other hand, are much more complicated. They are made up individually of decision trees, which can basically be represented by a set of rules. However, a random forest may have... | Use Random Forest model to make predictions from sensor data | Linear regressions are great because you can implement prediction very simply in any program that can multiply and add.
Random forests, on the other hand, are much more complicated. They are made up | Use Random Forest model to make predictions from sensor data
Linear regressions are great because you can implement prediction very simply in any program that can multiply and add.
Random forests, on the other hand, are much more complicated. They are made up individually of decision trees, which can basically be repr... | Use Random Forest model to make predictions from sensor data
Linear regressions are great because you can implement prediction very simply in any program that can multiply and add.
Random forests, on the other hand, are much more complicated. They are made up |
49,481 | Use Random Forest model to make predictions from sensor data | You certainly have to invest some work in that, but that's not really that bad to export the randomForest model; the getTree function dumps individual trees in a very compact and nice format like this (documented in ?getTree):
> getTree(iris_rf,3)
left daughter right daughter split var split point status prediction
... | Use Random Forest model to make predictions from sensor data | You certainly have to invest some work in that, but that's not really that bad to export the randomForest model; the getTree function dumps individual trees in a very compact and nice format like this | Use Random Forest model to make predictions from sensor data
You certainly have to invest some work in that, but that's not really that bad to export the randomForest model; the getTree function dumps individual trees in a very compact and nice format like this (documented in ?getTree):
> getTree(iris_rf,3)
left dau... | Use Random Forest model to make predictions from sensor data
You certainly have to invest some work in that, but that's not really that bad to export the randomForest model; the getTree function dumps individual trees in a very compact and nice format like this |
49,482 | Use Random Forest model to make predictions from sensor data | I recently developed a Python package that exports C code from Random Forests classifier trained with Scikit learn:
https://github.com/jonnor/emtrees
It could be used as an example of how to transform a R model to C code, by combined with the getTree shown by mbq answer. | Use Random Forest model to make predictions from sensor data | I recently developed a Python package that exports C code from Random Forests classifier trained with Scikit learn:
https://github.com/jonnor/emtrees
It could be used as an example of how to transfor | Use Random Forest model to make predictions from sensor data
I recently developed a Python package that exports C code from Random Forests classifier trained with Scikit learn:
https://github.com/jonnor/emtrees
It could be used as an example of how to transform a R model to C code, by combined with the getTree shown b... | Use Random Forest model to make predictions from sensor data
I recently developed a Python package that exports C code from Random Forests classifier trained with Scikit learn:
https://github.com/jonnor/emtrees
It could be used as an example of how to transfor |
49,483 | Which is the best accuracy measuring criteria among rmse, mae & mape? | I have to agree with Glen.
It is axiomatic in control system's engineering that there is no such thing as "best" without a measure of goodness.
Some (weak) examples of candidate bests include:
Best = robust indicator of central tendency
Best = robust indicator of variation around central tendency
Best = fastest to com... | Which is the best accuracy measuring criteria among rmse, mae & mape? | I have to agree with Glen.
It is axiomatic in control system's engineering that there is no such thing as "best" without a measure of goodness.
Some (weak) examples of candidate bests include:
Best = | Which is the best accuracy measuring criteria among rmse, mae & mape?
I have to agree with Glen.
It is axiomatic in control system's engineering that there is no such thing as "best" without a measure of goodness.
Some (weak) examples of candidate bests include:
Best = robust indicator of central tendency
Best = robus... | Which is the best accuracy measuring criteria among rmse, mae & mape?
I have to agree with Glen.
It is axiomatic in control system's engineering that there is no such thing as "best" without a measure of goodness.
Some (weak) examples of candidate bests include:
Best = |
49,484 | Kernel density estimator that doesn't collapse in the tails | It is a good idea to use the T-distribution to build a KDE
When you build a KDE, once you go outside the data range, the rate of decay in the tails is determined by the rate of decay in the tails of the kernel distribution. The normal distribution has very thin tails (which decay at an exponentially-quadratic rate) so... | Kernel density estimator that doesn't collapse in the tails | It is a good idea to use the T-distribution to build a KDE
When you build a KDE, once you go outside the data range, the rate of decay in the tails is determined by the rate of decay in the tails of t | Kernel density estimator that doesn't collapse in the tails
It is a good idea to use the T-distribution to build a KDE
When you build a KDE, once you go outside the data range, the rate of decay in the tails is determined by the rate of decay in the tails of the kernel distribution. The normal distribution has very th... | Kernel density estimator that doesn't collapse in the tails
It is a good idea to use the T-distribution to build a KDE
When you build a KDE, once you go outside the data range, the rate of decay in the tails is determined by the rate of decay in the tails of t |
49,485 | Kernel density estimator that doesn't collapse in the tails | It may be a consequence of the way you have presented your example, but it looks like your density function has finite support (e.g, a truncated Gaussian)? If this is the case, why not use a spline density estimator with linear tails:
http://cran.r-project.org/web/packages/pendensity/vignettes/pendensity.pdf
You could ... | Kernel density estimator that doesn't collapse in the tails | It may be a consequence of the way you have presented your example, but it looks like your density function has finite support (e.g, a truncated Gaussian)? If this is the case, why not use a spline de | Kernel density estimator that doesn't collapse in the tails
It may be a consequence of the way you have presented your example, but it looks like your density function has finite support (e.g, a truncated Gaussian)? If this is the case, why not use a spline density estimator with linear tails:
http://cran.r-project.org... | Kernel density estimator that doesn't collapse in the tails
It may be a consequence of the way you have presented your example, but it looks like your density function has finite support (e.g, a truncated Gaussian)? If this is the case, why not use a spline de |
49,486 | Kernel density estimator that doesn't collapse in the tails | I have a number of questions.
Why do you care about the tail of the distribution if you don't know where it is?
You have said that you would need to evaluate the density at x = 10 in your example. How many samples do you have? For KDE I am fairly certain you don't have enough for this. Looking at that part of the tail... | Kernel density estimator that doesn't collapse in the tails | I have a number of questions.
Why do you care about the tail of the distribution if you don't know where it is?
You have said that you would need to evaluate the density at x = 10 in your example. Ho | Kernel density estimator that doesn't collapse in the tails
I have a number of questions.
Why do you care about the tail of the distribution if you don't know where it is?
You have said that you would need to evaluate the density at x = 10 in your example. How many samples do you have? For KDE I am fairly certain you ... | Kernel density estimator that doesn't collapse in the tails
I have a number of questions.
Why do you care about the tail of the distribution if you don't know where it is?
You have said that you would need to evaluate the density at x = 10 in your example. Ho |
49,487 | Why is the logistic regression cost function scaled by the number of examples? | I think @soufanom had a good answer. I will try to add on.
In general, there are two reasons to have a constant in loss function.
The first reason is to have a simpler notation later. For example, you can have a loss function $f(y,\hat y)=\frac 1 2(y-\hat y)^2$, take the derivative respect to $y$, you will not have th... | Why is the logistic regression cost function scaled by the number of examples? | I think @soufanom had a good answer. I will try to add on.
In general, there are two reasons to have a constant in loss function.
The first reason is to have a simpler notation later. For example, yo | Why is the logistic regression cost function scaled by the number of examples?
I think @soufanom had a good answer. I will try to add on.
In general, there are two reasons to have a constant in loss function.
The first reason is to have a simpler notation later. For example, you can have a loss function $f(y,\hat y)=\... | Why is the logistic regression cost function scaled by the number of examples?
I think @soufanom had a good answer. I will try to add on.
In general, there are two reasons to have a constant in loss function.
The first reason is to have a simpler notation later. For example, yo |
49,488 | Why is the logistic regression cost function scaled by the number of examples? | Building further on the answer by @hxd1011:
Suppose we had a set model -- parameters and all fixed. We would look at the Mean Squared Error as our measure of error.
$MSE = \frac{1}{m}\sum_{i=1}^m (h_\theta(x^{(i)}) - y^{(i)})^2$
When we regularize using some function of our parameters we add a parameter of the form $\... | Why is the logistic regression cost function scaled by the number of examples? | Building further on the answer by @hxd1011:
Suppose we had a set model -- parameters and all fixed. We would look at the Mean Squared Error as our measure of error.
$MSE = \frac{1}{m}\sum_{i=1}^m (h_ | Why is the logistic regression cost function scaled by the number of examples?
Building further on the answer by @hxd1011:
Suppose we had a set model -- parameters and all fixed. We would look at the Mean Squared Error as our measure of error.
$MSE = \frac{1}{m}\sum_{i=1}^m (h_\theta(x^{(i)}) - y^{(i)})^2$
When we reg... | Why is the logistic regression cost function scaled by the number of examples?
Building further on the answer by @hxd1011:
Suppose we had a set model -- parameters and all fixed. We would look at the Mean Squared Error as our measure of error.
$MSE = \frac{1}{m}\sum_{i=1}^m (h_ |
49,489 | Duncan’s statistical test for blocks designed experiment with full factorial scheme | I think I have the answers for these questions:
Answers for questions (1), (2) and (3)
Since you want to compare averages between treatments, I would recommend to you to try first Tukey's test that is the most rigorous among the existent tests. Tukey's test is good if you want to avoid type I erros (reject null hypoth... | Duncan’s statistical test for blocks designed experiment with full factorial scheme | I think I have the answers for these questions:
Answers for questions (1), (2) and (3)
Since you want to compare averages between treatments, I would recommend to you to try first Tukey's test that is | Duncan’s statistical test for blocks designed experiment with full factorial scheme
I think I have the answers for these questions:
Answers for questions (1), (2) and (3)
Since you want to compare averages between treatments, I would recommend to you to try first Tukey's test that is the most rigorous among the existen... | Duncan’s statistical test for blocks designed experiment with full factorial scheme
I think I have the answers for these questions:
Answers for questions (1), (2) and (3)
Since you want to compare averages between treatments, I would recommend to you to try first Tukey's test that is |
49,490 | What's the best way to choose data for Crossvalidation on linear regression settings (PCA, PLS) | You want your evaluation to tell you something useful about your system's performance. Using a specific, held-out test set is nice because it tells you how the system will perform on totally new data. On the other hand, it's hard-to-impossible to perform meaningful inference (i.e., "In general, is my system better tha... | What's the best way to choose data for Crossvalidation on linear regression settings (PCA, PLS) | You want your evaluation to tell you something useful about your system's performance. Using a specific, held-out test set is nice because it tells you how the system will perform on totally new data | What's the best way to choose data for Crossvalidation on linear regression settings (PCA, PLS)
You want your evaluation to tell you something useful about your system's performance. Using a specific, held-out test set is nice because it tells you how the system will perform on totally new data. On the other hand, it'... | What's the best way to choose data for Crossvalidation on linear regression settings (PCA, PLS)
You want your evaluation to tell you something useful about your system's performance. Using a specific, held-out test set is nice because it tells you how the system will perform on totally new data |
49,491 | Update rule for beta distribution with fixed K/confidence/sample size | I've taken a look at the book. It seems to me that the rationale for this "prior sample size" teminology is the following. We have the usual model with $X_1,\dots,X_n$ conditionaly independent and identically distributed, given $\Theta=\theta$, with distribution $X_1\mid\Theta=\theta\sim\mathrm{Ber}(\theta)$. Suppose t... | Update rule for beta distribution with fixed K/confidence/sample size | I've taken a look at the book. It seems to me that the rationale for this "prior sample size" teminology is the following. We have the usual model with $X_1,\dots,X_n$ conditionaly independent and ide | Update rule for beta distribution with fixed K/confidence/sample size
I've taken a look at the book. It seems to me that the rationale for this "prior sample size" teminology is the following. We have the usual model with $X_1,\dots,X_n$ conditionaly independent and identically distributed, given $\Theta=\theta$, with ... | Update rule for beta distribution with fixed K/confidence/sample size
I've taken a look at the book. It seems to me that the rationale for this "prior sample size" teminology is the following. We have the usual model with $X_1,\dots,X_n$ conditionaly independent and ide |
49,492 | Update rule for beta distribution with fixed K/confidence/sample size | I like Zen's answer and want to add to it in order to clarify some misconceptions in the question. "Confidence" or "sample size" cannot be meaningfully applied to a posterior distribution. Only a likelihood can have a notion of "sample size". That's the motivation behind Zen's point that the confidence should be $a ... | Update rule for beta distribution with fixed K/confidence/sample size | I like Zen's answer and want to add to it in order to clarify some misconceptions in the question. "Confidence" or "sample size" cannot be meaningfully applied to a posterior distribution. Only a li | Update rule for beta distribution with fixed K/confidence/sample size
I like Zen's answer and want to add to it in order to clarify some misconceptions in the question. "Confidence" or "sample size" cannot be meaningfully applied to a posterior distribution. Only a likelihood can have a notion of "sample size". That... | Update rule for beta distribution with fixed K/confidence/sample size
I like Zen's answer and want to add to it in order to clarify some misconceptions in the question. "Confidence" or "sample size" cannot be meaningfully applied to a posterior distribution. Only a li |
49,493 | Weibull regression with known intercept in R | Without commenting on the validity of the method, from an R formula point of view, you can explicitly remove the intercept term by using -1 or +0
eg
something like
survreg(Surv(y)~x -1 + offset(rep(log(rweibull_scale),length(x))), scale=1/rweibull_shape)
or
survreg(Surv(y)~x+0 + offset(rep(log(rweibull_scale),length(x... | Weibull regression with known intercept in R | Without commenting on the validity of the method, from an R formula point of view, you can explicitly remove the intercept term by using -1 or +0
eg
something like
survreg(Surv(y)~x -1 + offset(rep(lo | Weibull regression with known intercept in R
Without commenting on the validity of the method, from an R formula point of view, you can explicitly remove the intercept term by using -1 or +0
eg
something like
survreg(Surv(y)~x -1 + offset(rep(log(rweibull_scale),length(x))), scale=1/rweibull_shape)
or
survreg(Surv(y)~... | Weibull regression with known intercept in R
Without commenting on the validity of the method, from an R formula point of view, you can explicitly remove the intercept term by using -1 or +0
eg
something like
survreg(Surv(y)~x -1 + offset(rep(lo |
49,494 | Two sequences, one HMM | If you'd like to know the theory of doing this, it's covered in Rabiner's great paper "A tutorial to Hidden Markov models and selected applications in speech recognition" (Proc of the IEEE, 1989, 77(2), p.273; the full text available on multiple websites online - just google the name). As for whether there is an implem... | Two sequences, one HMM | If you'd like to know the theory of doing this, it's covered in Rabiner's great paper "A tutorial to Hidden Markov models and selected applications in speech recognition" (Proc of the IEEE, 1989, 77(2 | Two sequences, one HMM
If you'd like to know the theory of doing this, it's covered in Rabiner's great paper "A tutorial to Hidden Markov models and selected applications in speech recognition" (Proc of the IEEE, 1989, 77(2), p.273; the full text available on multiple websites online - just google the name). As for whe... | Two sequences, one HMM
If you'd like to know the theory of doing this, it's covered in Rabiner's great paper "A tutorial to Hidden Markov models and selected applications in speech recognition" (Proc of the IEEE, 1989, 77(2 |
49,495 | Validation: Data splitting into training vs. test datasets | The split sample validation you proposed above has become less popular in many fields because of the issue Harrell mentions (unreliable out of bag estimates). I know Harrell has mentioned this in his textbook, but other references would be Steyerberg "Clinical Prediction Models" p301, James et al "An Introduction to St... | Validation: Data splitting into training vs. test datasets | The split sample validation you proposed above has become less popular in many fields because of the issue Harrell mentions (unreliable out of bag estimates). I know Harrell has mentioned this in his | Validation: Data splitting into training vs. test datasets
The split sample validation you proposed above has become less popular in many fields because of the issue Harrell mentions (unreliable out of bag estimates). I know Harrell has mentioned this in his textbook, but other references would be Steyerberg "Clinical ... | Validation: Data splitting into training vs. test datasets
The split sample validation you proposed above has become less popular in many fields because of the issue Harrell mentions (unreliable out of bag estimates). I know Harrell has mentioned this in his |
49,496 | Cumulative counts or counts for Poisson regression | The approach generally taken is to regress the counts on features that were present during the intervals during which the counts accumulated. The length of the interval is used as an offset after applying log() to the values to match the default link for a Poisson model. The data situation described does not justify an... | Cumulative counts or counts for Poisson regression | The approach generally taken is to regress the counts on features that were present during the intervals during which the counts accumulated. The length of the interval is used as an offset after appl | Cumulative counts or counts for Poisson regression
The approach generally taken is to regress the counts on features that were present during the intervals during which the counts accumulated. The length of the interval is used as an offset after applying log() to the values to match the default link for a Poisson mode... | Cumulative counts or counts for Poisson regression
The approach generally taken is to regress the counts on features that were present during the intervals during which the counts accumulated. The length of the interval is used as an offset after appl |
49,497 | Testing whether there is an increase between two regression slopes in a time series | I feel that after three years it does no harm to post my own answer to how I solved this problem. It could be the case I have made an error or two, so please use with a pinch of salt!
Following whuber's advice in the comments, since my data is a time series of heights we would expect a continuity of the height at the c... | Testing whether there is an increase between two regression slopes in a time series | I feel that after three years it does no harm to post my own answer to how I solved this problem. It could be the case I have made an error or two, so please use with a pinch of salt!
Following whuber | Testing whether there is an increase between two regression slopes in a time series
I feel that after three years it does no harm to post my own answer to how I solved this problem. It could be the case I have made an error or two, so please use with a pinch of salt!
Following whuber's advice in the comments, since my ... | Testing whether there is an increase between two regression slopes in a time series
I feel that after three years it does no harm to post my own answer to how I solved this problem. It could be the case I have made an error or two, so please use with a pinch of salt!
Following whuber |
49,498 | Testing whether there is an increase between two regression slopes in a time series | The tests that habe been suggested are limited to comparing the two slopes BUT the slopes could be the same and the two ontercepts could be different. The Chow Test http://en.wikipedia.org/wiki/Chow_test tests the significance of both coefficients not just the slope. Some software will actually find the breakpoint (if ... | Testing whether there is an increase between two regression slopes in a time series | The tests that habe been suggested are limited to comparing the two slopes BUT the slopes could be the same and the two ontercepts could be different. The Chow Test http://en.wikipedia.org/wiki/Chow_t | Testing whether there is an increase between two regression slopes in a time series
The tests that habe been suggested are limited to comparing the two slopes BUT the slopes could be the same and the two ontercepts could be different. The Chow Test http://en.wikipedia.org/wiki/Chow_test tests the significance of both c... | Testing whether there is an increase between two regression slopes in a time series
The tests that habe been suggested are limited to comparing the two slopes BUT the slopes could be the same and the two ontercepts could be different. The Chow Test http://en.wikipedia.org/wiki/Chow_t |
49,499 | Confusion related to generalized linear model example | My less-technical explanation: Actually, you are approaching their answer. You realize that the formula for a large beach doesn't work well for a small beach, so you make a second model. But neither of these models will work well for a medium beach, so you add a third. None of these works well for a huge beach, so you ... | Confusion related to generalized linear model example | My less-technical explanation: Actually, you are approaching their answer. You realize that the formula for a large beach doesn't work well for a small beach, so you make a second model. But neither o | Confusion related to generalized linear model example
My less-technical explanation: Actually, you are approaching their answer. You realize that the formula for a large beach doesn't work well for a small beach, so you make a second model. But neither of these models will work well for a medium beach, so you add a thi... | Confusion related to generalized linear model example
My less-technical explanation: Actually, you are approaching their answer. You realize that the formula for a large beach doesn't work well for a small beach, so you make a second model. But neither o |
49,500 | Confusion related to generalized linear model example | The 'geometric' change with constant input means that it will change by a constant proportion, or factor, rather than by a constant additive amount. For example, if the old value is $y_{old}$ and $x$ goes up by 1, there will be an $\exp(\beta)$-fold change in $y$. That is, the new $y_{new}=\exp(\beta)y_{old}$. On th... | Confusion related to generalized linear model example | The 'geometric' change with constant input means that it will change by a constant proportion, or factor, rather than by a constant additive amount. For example, if the old value is $y_{old}$ and $x$ | Confusion related to generalized linear model example
The 'geometric' change with constant input means that it will change by a constant proportion, or factor, rather than by a constant additive amount. For example, if the old value is $y_{old}$ and $x$ goes up by 1, there will be an $\exp(\beta)$-fold change in $y$. ... | Confusion related to generalized linear model example
The 'geometric' change with constant input means that it will change by a constant proportion, or factor, rather than by a constant additive amount. For example, if the old value is $y_{old}$ and $x$ |
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