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 |
|---|---|---|---|---|---|---|
51,301 | What is the VC dimension of a decision tree? | I'm not sure this is a question with a simple answer, nor do I believe it is a question that even needs to be asked about decision trees.
Consult Aslan et al., Calculating the VC-Dimension of Trees (2009). They address this problem by doing an exhaustive search, in small trees, and then providing an approximate, recurs... | What is the VC dimension of a decision tree? | I'm not sure this is a question with a simple answer, nor do I believe it is a question that even needs to be asked about decision trees.
Consult Aslan et al., Calculating the VC-Dimension of Trees (2 | What is the VC dimension of a decision tree?
I'm not sure this is a question with a simple answer, nor do I believe it is a question that even needs to be asked about decision trees.
Consult Aslan et al., Calculating the VC-Dimension of Trees (2009). They address this problem by doing an exhaustive search, in small tre... | What is the VC dimension of a decision tree?
I'm not sure this is a question with a simple answer, nor do I believe it is a question that even needs to be asked about decision trees.
Consult Aslan et al., Calculating the VC-Dimension of Trees (2 |
51,302 | What is the VC dimension of a decision tree? | I know this post is kind of old and already has an accepted answered, but as it is the first to link appear on Google when asking about the VC dimension of decision trees, I will allow myself to give some new information as a follow up.
In a recent paper, Decision trees as partitioning machines to characterize their ge... | What is the VC dimension of a decision tree? | I know this post is kind of old and already has an accepted answered, but as it is the first to link appear on Google when asking about the VC dimension of decision trees, I will allow myself to give | What is the VC dimension of a decision tree?
I know this post is kind of old and already has an accepted answered, but as it is the first to link appear on Google when asking about the VC dimension of decision trees, I will allow myself to give some new information as a follow up.
In a recent paper, Decision trees as p... | What is the VC dimension of a decision tree?
I know this post is kind of old and already has an accepted answered, but as it is the first to link appear on Google when asking about the VC dimension of decision trees, I will allow myself to give |
51,303 | Is there a t-test equivalent to Stouffer's z-test? | According to Becker's chapter on combining $p$-values in Cooper and Hedges book you can use
$$
\frac{\sum {t_f}_i (p_i)}{{\sqrt{\sum\frac{f_i}{f_i-2}}}} > z(\alpha)
$$
where ${t_f}_i$ is Student's $t$ with $f_i$ the degrees of freedom $p_i$ the p-value and $\alpha$ is the desired significance value.
She does not give a... | Is there a t-test equivalent to Stouffer's z-test? | According to Becker's chapter on combining $p$-values in Cooper and Hedges book you can use
$$
\frac{\sum {t_f}_i (p_i)}{{\sqrt{\sum\frac{f_i}{f_i-2}}}} > z(\alpha)
$$
where ${t_f}_i$ is Student's $t$ | Is there a t-test equivalent to Stouffer's z-test?
According to Becker's chapter on combining $p$-values in Cooper and Hedges book you can use
$$
\frac{\sum {t_f}_i (p_i)}{{\sqrt{\sum\frac{f_i}{f_i-2}}}} > z(\alpha)
$$
where ${t_f}_i$ is Student's $t$ with $f_i$ the degrees of freedom $p_i$ the p-value and $\alpha$ is ... | Is there a t-test equivalent to Stouffer's z-test?
According to Becker's chapter on combining $p$-values in Cooper and Hedges book you can use
$$
\frac{\sum {t_f}_i (p_i)}{{\sqrt{\sum\frac{f_i}{f_i-2}}}} > z(\alpha)
$$
where ${t_f}_i$ is Student's $t$ |
51,304 | Tensorflow loss not changing and also computed gradients and applied batch norm but still loss is not changing? [closed] | I jave just basic knowledge of tensorflow but I will try to guess the issue there.
Seems like the problem lies in the calls:
For conv2 to conv5:
conv = tf.nn.conv2d(outputs_fed_lstm, conv_weights, strides=[1,1,1,1], padding = "VALID")
You want to access outputs_fed_lstm declared within scope of conv4 so should be somet... | Tensorflow loss not changing and also computed gradients and applied batch norm but still loss is no | I jave just basic knowledge of tensorflow but I will try to guess the issue there.
Seems like the problem lies in the calls:
For conv2 to conv5:
conv = tf.nn.conv2d(outputs_fed_lstm, conv_weights, str | Tensorflow loss not changing and also computed gradients and applied batch norm but still loss is not changing? [closed]
I jave just basic knowledge of tensorflow but I will try to guess the issue there.
Seems like the problem lies in the calls:
For conv2 to conv5:
conv = tf.nn.conv2d(outputs_fed_lstm, conv_weights, st... | Tensorflow loss not changing and also computed gradients and applied batch norm but still loss is no
I jave just basic knowledge of tensorflow but I will try to guess the issue there.
Seems like the problem lies in the calls:
For conv2 to conv5:
conv = tf.nn.conv2d(outputs_fed_lstm, conv_weights, str |
51,305 | Row Correlation Heatmap Pandas | Answered in comments by Andy W:
Sorting the correlation matrix may provide clusters of variables, see
here for one description of how to sort them | Row Correlation Heatmap Pandas | Answered in comments by Andy W:
Sorting the correlation matrix may provide clusters of variables, see
here for one description of how to sort them | Row Correlation Heatmap Pandas
Answered in comments by Andy W:
Sorting the correlation matrix may provide clusters of variables, see
here for one description of how to sort them | Row Correlation Heatmap Pandas
Answered in comments by Andy W:
Sorting the correlation matrix may provide clusters of variables, see
here for one description of how to sort them |
51,306 | R's lmer cheat sheet | What's the difference between (~1 +....) and (1 | ...) and (0 | ...) etc.?
Say you have variable V1 predicted by categorical variable V2, which is treated as a random effect, and continuous variable V3, which is treated as a linear fixed effect. Using lmer syntax, simplest model (M1) is:
V1 ~ (1|V2) + V3
This model w... | R's lmer cheat sheet | What's the difference between (~1 +....) and (1 | ...) and (0 | ...) etc.?
Say you have variable V1 predicted by categorical variable V2, which is treated as a random effect, and continuous variable | R's lmer cheat sheet
What's the difference between (~1 +....) and (1 | ...) and (0 | ...) etc.?
Say you have variable V1 predicted by categorical variable V2, which is treated as a random effect, and continuous variable V3, which is treated as a linear fixed effect. Using lmer syntax, simplest model (M1) is:
V1 ~ (1|V... | R's lmer cheat sheet
What's the difference between (~1 +....) and (1 | ...) and (0 | ...) etc.?
Say you have variable V1 predicted by categorical variable V2, which is treated as a random effect, and continuous variable |
51,307 | R's lmer cheat sheet | The general trick is, as mentioned in another answer, is that the formula follows the form dependent ~ independent | grouping. The groupingis generally a random factor, you can include fixed factors without any grouping and you can have additional random factors without any fixed factor (an intercept-only model). A + ... | R's lmer cheat sheet | The general trick is, as mentioned in another answer, is that the formula follows the form dependent ~ independent | grouping. The groupingis generally a random factor, you can include fixed factors w | R's lmer cheat sheet
The general trick is, as mentioned in another answer, is that the formula follows the form dependent ~ independent | grouping. The groupingis generally a random factor, you can include fixed factors without any grouping and you can have additional random factors without any fixed factor (an interce... | R's lmer cheat sheet
The general trick is, as mentioned in another answer, is that the formula follows the form dependent ~ independent | grouping. The groupingis generally a random factor, you can include fixed factors w |
51,308 | R's lmer cheat sheet | The | symbol indicates a grouping factor in mixed methods.
As per Pinheiro & Bates:
...The formula also designates a response and, when available, a primary covariate. It is given as
response ~ primary | grouping
where response is an expression for the response, primary is an expression for the primary covariate, and... | R's lmer cheat sheet | The | symbol indicates a grouping factor in mixed methods.
As per Pinheiro & Bates:
...The formula also designates a response and, when available, a primary covariate. It is given as
response ~ prima | R's lmer cheat sheet
The | symbol indicates a grouping factor in mixed methods.
As per Pinheiro & Bates:
...The formula also designates a response and, when available, a primary covariate. It is given as
response ~ primary | grouping
where response is an expression for the response, primary is an expression for the p... | R's lmer cheat sheet
The | symbol indicates a grouping factor in mixed methods.
As per Pinheiro & Bates:
...The formula also designates a response and, when available, a primary covariate. It is given as
response ~ prima |
51,309 | Why is multiple comparison a problem? | You've stated something that is a classic counter argument to Bonferroni corrections. Shouldn't I adjust my alpha criterion based on every test I will ever make? This kind of ad absurdum implication is why some people do not believe in Bonferroni style corrections at all. Sometimes the kind of data one deals with in... | Why is multiple comparison a problem? | You've stated something that is a classic counter argument to Bonferroni corrections. Shouldn't I adjust my alpha criterion based on every test I will ever make? This kind of ad absurdum implication | Why is multiple comparison a problem?
You've stated something that is a classic counter argument to Bonferroni corrections. Shouldn't I adjust my alpha criterion based on every test I will ever make? This kind of ad absurdum implication is why some people do not believe in Bonferroni style corrections at all. Someti... | Why is multiple comparison a problem?
You've stated something that is a classic counter argument to Bonferroni corrections. Shouldn't I adjust my alpha criterion based on every test I will ever make? This kind of ad absurdum implication |
51,310 | Why is multiple comparison a problem? | Well-respected statisticians have taken a wide variety of positions on multiple comparisons. It's a subtle subject. If someone thinks it's simple, I'd wonder how much they've thought about it.
Here's an interesting Bayesian perspective on multiple testing from Andrew Gelman: Why we don't (usually) worry about multiple... | Why is multiple comparison a problem? | Well-respected statisticians have taken a wide variety of positions on multiple comparisons. It's a subtle subject. If someone thinks it's simple, I'd wonder how much they've thought about it.
Here's | Why is multiple comparison a problem?
Well-respected statisticians have taken a wide variety of positions on multiple comparisons. It's a subtle subject. If someone thinks it's simple, I'd wonder how much they've thought about it.
Here's an interesting Bayesian perspective on multiple testing from Andrew Gelman: Why w... | Why is multiple comparison a problem?
Well-respected statisticians have taken a wide variety of positions on multiple comparisons. It's a subtle subject. If someone thinks it's simple, I'd wonder how much they've thought about it.
Here's |
51,311 | Why is multiple comparison a problem? | Related to the comment earlier, what the fMRI researcher should remember is that clinically-important outcomes are what matter, not the density shift of a single pixel on a fMRI of the brain. If it doesn't result in a clinical improvement/detriment, it doesn't matter. That is one way of reducing the concern about multi... | Why is multiple comparison a problem? | Related to the comment earlier, what the fMRI researcher should remember is that clinically-important outcomes are what matter, not the density shift of a single pixel on a fMRI of the brain. If it do | Why is multiple comparison a problem?
Related to the comment earlier, what the fMRI researcher should remember is that clinically-important outcomes are what matter, not the density shift of a single pixel on a fMRI of the brain. If it doesn't result in a clinical improvement/detriment, it doesn't matter. That is one w... | Why is multiple comparison a problem?
Related to the comment earlier, what the fMRI researcher should remember is that clinically-important outcomes are what matter, not the density shift of a single pixel on a fMRI of the brain. If it do |
51,312 | Why is multiple comparison a problem? | To fix ideas: I will take the case when you obverse, $n$ independent random variables $(X_i)_{i=1,\dots,n}$ such that for $i=1,\dots,n$ $X_i$ is drawn from $\mathcal{N}(\theta_i,1)$. I assume that you want to know which one have non zero mean, formally you want to test:
$H_{0i} : \theta_i=0$ Vs $H_{1i} : \theta_i\neq ... | Why is multiple comparison a problem? | To fix ideas: I will take the case when you obverse, $n$ independent random variables $(X_i)_{i=1,\dots,n}$ such that for $i=1,\dots,n$ $X_i$ is drawn from $\mathcal{N}(\theta_i,1)$. I assume that yo | Why is multiple comparison a problem?
To fix ideas: I will take the case when you obverse, $n$ independent random variables $(X_i)_{i=1,\dots,n}$ such that for $i=1,\dots,n$ $X_i$ is drawn from $\mathcal{N}(\theta_i,1)$. I assume that you want to know which one have non zero mean, formally you want to test:
$H_{0i} : ... | Why is multiple comparison a problem?
To fix ideas: I will take the case when you obverse, $n$ independent random variables $(X_i)_{i=1,\dots,n}$ such that for $i=1,\dots,n$ $X_i$ is drawn from $\mathcal{N}(\theta_i,1)$. I assume that yo |
51,313 | Why is multiple comparison a problem? | An illustrating (and funny) article (http://www.jsur.org/ar/jsur_ben102010.pdf) about the need to correct for multiple testing in some practical study evolving many variables e.g. functional MRI (fMRI). This short citation contains most of the message:
"[...] we completed an fMRI scanning session with a post-mortem... | Why is multiple comparison a problem? | An illustrating (and funny) article (http://www.jsur.org/ar/jsur_ben102010.pdf) about the need to correct for multiple testing in some practical study evolving many variables e.g. functional MRI (fMR | Why is multiple comparison a problem?
An illustrating (and funny) article (http://www.jsur.org/ar/jsur_ben102010.pdf) about the need to correct for multiple testing in some practical study evolving many variables e.g. functional MRI (fMRI). This short citation contains most of the message:
"[...] we completed an fM... | Why is multiple comparison a problem?
An illustrating (and funny) article (http://www.jsur.org/ar/jsur_ben102010.pdf) about the need to correct for multiple testing in some practical study evolving many variables e.g. functional MRI (fMR |
51,314 | why sample variance has has n-1 in the denominator? [duplicate] | To put it simply $(n-1)$ is a smaller number than $(n)$. When you divide by a smaller number you get a larger number. Therefore when you divide by $(n-1)$ the sample variance will work out to be a larger number.
Let's think about what a larger vs. smaller sample variance means. If the sample variance is larger than th... | why sample variance has has n-1 in the denominator? [duplicate] | To put it simply $(n-1)$ is a smaller number than $(n)$. When you divide by a smaller number you get a larger number. Therefore when you divide by $(n-1)$ the sample variance will work out to be a lar | why sample variance has has n-1 in the denominator? [duplicate]
To put it simply $(n-1)$ is a smaller number than $(n)$. When you divide by a smaller number you get a larger number. Therefore when you divide by $(n-1)$ the sample variance will work out to be a larger number.
Let's think about what a larger vs. smaller... | why sample variance has has n-1 in the denominator? [duplicate]
To put it simply $(n-1)$ is a smaller number than $(n)$. When you divide by a smaller number you get a larger number. Therefore when you divide by $(n-1)$ the sample variance will work out to be a lar |
51,315 | Kruskal Wallis or MANOVA | For 8 input variables and 8 outcome variables, you need multivariate multiple regression or MANCOVA.
MANOVA is used in case of one input and multiple outcomes. | Kruskal Wallis or MANOVA | For 8 input variables and 8 outcome variables, you need multivariate multiple regression or MANCOVA.
MANOVA is used in case of one input and multiple outcomes. | Kruskal Wallis or MANOVA
For 8 input variables and 8 outcome variables, you need multivariate multiple regression or MANCOVA.
MANOVA is used in case of one input and multiple outcomes. | Kruskal Wallis or MANOVA
For 8 input variables and 8 outcome variables, you need multivariate multiple regression or MANCOVA.
MANOVA is used in case of one input and multiple outcomes. |
51,316 | Kruskal Wallis or MANOVA | Can I ask what your sample size is? Are you planning on running all those tests?
Is there no other way of reorganizing your data?
If not, why not use the following multinomial model:
DV = IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7
where each of your nominal categories are dummy variables (1,0) and your IV 8 is your base... | Kruskal Wallis or MANOVA | Can I ask what your sample size is? Are you planning on running all those tests?
Is there no other way of reorganizing your data?
If not, why not use the following multinomial model:
DV = IV1 + IV2 + | Kruskal Wallis or MANOVA
Can I ask what your sample size is? Are you planning on running all those tests?
Is there no other way of reorganizing your data?
If not, why not use the following multinomial model:
DV = IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7
where each of your nominal categories are dummy variables (1,0) a... | Kruskal Wallis or MANOVA
Can I ask what your sample size is? Are you planning on running all those tests?
Is there no other way of reorganizing your data?
If not, why not use the following multinomial model:
DV = IV1 + IV2 + |
51,317 | Comparing smoothing splines vs loess for smoothing? | Here is some R code/example that will let you compare the fits for a loess fit and a spline fit:
library(TeachingDemos)
library(splines)
tmpfun <- function(x,y,span=.75,df=3) {
plot(x,y)
fit1 <- lm(y ~ ns(x,df))
xx <- seq( min(x), max(x), length.out=250 )
yy <- predict(fit1, data.frame(x=xx))
lines... | Comparing smoothing splines vs loess for smoothing? | Here is some R code/example that will let you compare the fits for a loess fit and a spline fit:
library(TeachingDemos)
library(splines)
tmpfun <- function(x,y,span=.75,df=3) {
plot(x,y)
fit1 | Comparing smoothing splines vs loess for smoothing?
Here is some R code/example that will let you compare the fits for a loess fit and a spline fit:
library(TeachingDemos)
library(splines)
tmpfun <- function(x,y,span=.75,df=3) {
plot(x,y)
fit1 <- lm(y ~ ns(x,df))
xx <- seq( min(x), max(x), length.out=250 )... | Comparing smoothing splines vs loess for smoothing?
Here is some R code/example that will let you compare the fits for a loess fit and a spline fit:
library(TeachingDemos)
library(splines)
tmpfun <- function(x,y,span=.75,df=3) {
plot(x,y)
fit1 |
51,318 | Comparing smoothing splines vs loess for smoothing? | The actual results from a smoothing spline or loess are going to be pretty similar. They might look a little different at the edges of the support, but as long as you make sure it's a "natural" smoothing spline they will look really similar.
If you are just using one to add a "smoother" to a scatterplot, there's no re... | Comparing smoothing splines vs loess for smoothing? | The actual results from a smoothing spline or loess are going to be pretty similar. They might look a little different at the edges of the support, but as long as you make sure it's a "natural" smoot | Comparing smoothing splines vs loess for smoothing?
The actual results from a smoothing spline or loess are going to be pretty similar. They might look a little different at the edges of the support, but as long as you make sure it's a "natural" smoothing spline they will look really similar.
If you are just using one... | Comparing smoothing splines vs loess for smoothing?
The actual results from a smoothing spline or loess are going to be pretty similar. They might look a little different at the edges of the support, but as long as you make sure it's a "natural" smoot |
51,319 | Linear Regression - Confidence interval for mean response vs prediction interval | I think one is conditional to x (at one value of x, you confidence statement is correct), and the other is simultaneous for the entire regression line support. The second should be larger due to "multiplicity". | Linear Regression - Confidence interval for mean response vs prediction interval | I think one is conditional to x (at one value of x, you confidence statement is correct), and the other is simultaneous for the entire regression line support. The second should be larger due to "mult | Linear Regression - Confidence interval for mean response vs prediction interval
I think one is conditional to x (at one value of x, you confidence statement is correct), and the other is simultaneous for the entire regression line support. The second should be larger due to "multiplicity". | Linear Regression - Confidence interval for mean response vs prediction interval
I think one is conditional to x (at one value of x, you confidence statement is correct), and the other is simultaneous for the entire regression line support. The second should be larger due to "mult |
51,320 | How to "trick" R into producing treatment effects in a treatment * dummy model? | I have to struggle a bit with the mathematical notation, if I but in the following formula that you provided:
$$y = \delta_0 + \sum_{j=1}^n\beta_j D_j + \sum_{i=1}^m \sum_{j=0}^n \gamma_{i,j}T_iD_j$$
Isn't that essentially saying that there should be one coefficient per dummy variable (excluding the first), and then on... | How to "trick" R into producing treatment effects in a treatment * dummy model? | I have to struggle a bit with the mathematical notation, if I but in the following formula that you provided:
$$y = \delta_0 + \sum_{j=1}^n\beta_j D_j + \sum_{i=1}^m \sum_{j=0}^n \gamma_{i,j}T_iD_j$$
| How to "trick" R into producing treatment effects in a treatment * dummy model?
I have to struggle a bit with the mathematical notation, if I but in the following formula that you provided:
$$y = \delta_0 + \sum_{j=1}^n\beta_j D_j + \sum_{i=1}^m \sum_{j=0}^n \gamma_{i,j}T_iD_j$$
Isn't that essentially saying that there... | How to "trick" R into producing treatment effects in a treatment * dummy model?
I have to struggle a bit with the mathematical notation, if I but in the following formula that you provided:
$$y = \delta_0 + \sum_{j=1}^n\beta_j D_j + \sum_{i=1}^m \sum_{j=0}^n \gamma_{i,j}T_iD_j$$
|
51,321 | Structural Equation Modeling Two-Step Method | Do I get a Measurement Model set in stone, then proceed to estimate the entire model all together?
Yes, their idea was to first fix measurement-model misspecifications, then to begin evaluating fit of the structural model given a well-fitting measurement model.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equa... | Structural Equation Modeling Two-Step Method | Do I get a Measurement Model set in stone, then proceed to estimate the entire model all together?
Yes, their idea was to first fix measurement-model misspecifications, then to begin evaluating fit o | Structural Equation Modeling Two-Step Method
Do I get a Measurement Model set in stone, then proceed to estimate the entire model all together?
Yes, their idea was to first fix measurement-model misspecifications, then to begin evaluating fit of the structural model given a well-fitting measurement model.
Anderson, J.... | Structural Equation Modeling Two-Step Method
Do I get a Measurement Model set in stone, then proceed to estimate the entire model all together?
Yes, their idea was to first fix measurement-model misspecifications, then to begin evaluating fit o |
51,322 | Measures of variable importance in random forests | The first one can be 'interpreted' as follows: if a predictor is important in your current model, then assigning other values for that predictor randomly but 'realistically' (i.e.: permuting this predictor's values over your dataset), should have a negative influence on prediction, i.e.: using the same model to predict... | Measures of variable importance in random forests | The first one can be 'interpreted' as follows: if a predictor is important in your current model, then assigning other values for that predictor randomly but 'realistically' (i.e.: permuting this pred | Measures of variable importance in random forests
The first one can be 'interpreted' as follows: if a predictor is important in your current model, then assigning other values for that predictor randomly but 'realistically' (i.e.: permuting this predictor's values over your dataset), should have a negative influence on... | Measures of variable importance in random forests
The first one can be 'interpreted' as follows: if a predictor is important in your current model, then assigning other values for that predictor randomly but 'realistically' (i.e.: permuting this pred |
51,323 | Measures of variable importance in random forests | Random Forest importance metrics as implemented in the randomForest package in R have quirks in that correlated predictors get low importance values.
http://bioinformatics.oxfordjournals.org/content/early/2010/04/12/bioinformatics.btq134.full.pdf
I have a modified implementation of random forests out on CRAN which imp... | Measures of variable importance in random forests | Random Forest importance metrics as implemented in the randomForest package in R have quirks in that correlated predictors get low importance values.
http://bioinformatics.oxfordjournals.org/content/ | Measures of variable importance in random forests
Random Forest importance metrics as implemented in the randomForest package in R have quirks in that correlated predictors get low importance values.
http://bioinformatics.oxfordjournals.org/content/early/2010/04/12/bioinformatics.btq134.full.pdf
I have a modified impl... | Measures of variable importance in random forests
Random Forest importance metrics as implemented in the randomForest package in R have quirks in that correlated predictors get low importance values.
http://bioinformatics.oxfordjournals.org/content/ |
51,324 | GEE model returns GLM results | The parameter estimates of the intercept and slope are the same for GLM and GEE, which is expected. The standard errors, however, are different, which is also expected. GEE used the exchangeable covariance to adjust the standard errors in order to account for the correlation within ID that was induced in the $Y$ value... | GEE model returns GLM results | The parameter estimates of the intercept and slope are the same for GLM and GEE, which is expected. The standard errors, however, are different, which is also expected. GEE used the exchangeable cova | GEE model returns GLM results
The parameter estimates of the intercept and slope are the same for GLM and GEE, which is expected. The standard errors, however, are different, which is also expected. GEE used the exchangeable covariance to adjust the standard errors in order to account for the correlation within ID tha... | GEE model returns GLM results
The parameter estimates of the intercept and slope are the same for GLM and GEE, which is expected. The standard errors, however, are different, which is also expected. GEE used the exchangeable cova |
51,325 | GEE model returns GLM results | Might know the answer. GEE won't work if your id variable is not 'sorted' in the data frame. Check to make sure it's 'clustered' in order.
From the GEE documentation: "id: a vector which identifies the clusters. The length of id should be the same as the number of observations. Data are assumed to be sorted so that ob... | GEE model returns GLM results | Might know the answer. GEE won't work if your id variable is not 'sorted' in the data frame. Check to make sure it's 'clustered' in order.
From the GEE documentation: "id: a vector which identifies t | GEE model returns GLM results
Might know the answer. GEE won't work if your id variable is not 'sorted' in the data frame. Check to make sure it's 'clustered' in order.
From the GEE documentation: "id: a vector which identifies the clusters. The length of id should be the same as the number of observations. Data are a... | GEE model returns GLM results
Might know the answer. GEE won't work if your id variable is not 'sorted' in the data frame. Check to make sure it's 'clustered' in order.
From the GEE documentation: "id: a vector which identifies t |
51,326 | Do we have to tune the number of trees in a random forest? | It's common to find code snippets that treat $T$ as a hyper-parameter, and attempt to optimize over it in the same way as any other hyper-parameter. This is just wasting computational power: when all other hyper-parameters are fixed, the model’s loss stochastically decreases as the number of trees increases.
Intuitive ... | Do we have to tune the number of trees in a random forest? | It's common to find code snippets that treat $T$ as a hyper-parameter, and attempt to optimize over it in the same way as any other hyper-parameter. This is just wasting computational power: when all | Do we have to tune the number of trees in a random forest?
It's common to find code snippets that treat $T$ as a hyper-parameter, and attempt to optimize over it in the same way as any other hyper-parameter. This is just wasting computational power: when all other hyper-parameters are fixed, the model’s loss stochastic... | Do we have to tune the number of trees in a random forest?
It's common to find code snippets that treat $T$ as a hyper-parameter, and attempt to optimize over it in the same way as any other hyper-parameter. This is just wasting computational power: when all |
51,327 | data are drawn from a probability distribution P? | A random variable is something that takes different values where there is some randomness to the value it can take. A probability distribution assigns a probability to each possible outcome of that random variable. In your case, you are observing data that could've been different. In other words, if you took another sa... | data are drawn from a probability distribution P? | A random variable is something that takes different values where there is some randomness to the value it can take. A probability distribution assigns a probability to each possible outcome of that ra | data are drawn from a probability distribution P?
A random variable is something that takes different values where there is some randomness to the value it can take. A probability distribution assigns a probability to each possible outcome of that random variable. In your case, you are observing data that could've been... | data are drawn from a probability distribution P?
A random variable is something that takes different values where there is some randomness to the value it can take. A probability distribution assigns a probability to each possible outcome of that ra |
51,328 | data are drawn from a probability distribution P? | A probability distribution assigns likelihoods to the values in its domain.
A good way to think about it is a six-sided dice roll. Dice assign probabilities to each of the sides: we have a 1 in 6 chance of seeing each side. However, in practice, we would roll the dice 6 times and are not likely to see all 6 sides.
I... | data are drawn from a probability distribution P? | A probability distribution assigns likelihoods to the values in its domain.
A good way to think about it is a six-sided dice roll. Dice assign probabilities to each of the sides: we have a 1 in 6 cha | data are drawn from a probability distribution P?
A probability distribution assigns likelihoods to the values in its domain.
A good way to think about it is a six-sided dice roll. Dice assign probabilities to each of the sides: we have a 1 in 6 chance of seeing each side. However, in practice, we would roll the dice... | data are drawn from a probability distribution P?
A probability distribution assigns likelihoods to the values in its domain.
A good way to think about it is a six-sided dice roll. Dice assign probabilities to each of the sides: we have a 1 in 6 cha |
51,329 | data are drawn from a probability distribution P? | Typically, it means that you make a computer generate pseudorandom numbers between 0 and 1, which is then used as input in the inverse of the cumulative density function (CDF) of the distribution P.
The image below shows the CDF for the normal distribution with mean = 0 and standard deviation = 1:
The computer is gene... | data are drawn from a probability distribution P? | Typically, it means that you make a computer generate pseudorandom numbers between 0 and 1, which is then used as input in the inverse of the cumulative density function (CDF) of the distribution P.
T | data are drawn from a probability distribution P?
Typically, it means that you make a computer generate pseudorandom numbers between 0 and 1, which is then used as input in the inverse of the cumulative density function (CDF) of the distribution P.
The image below shows the CDF for the normal distribution with mean = 0... | data are drawn from a probability distribution P?
Typically, it means that you make a computer generate pseudorandom numbers between 0 and 1, which is then used as input in the inverse of the cumulative density function (CDF) of the distribution P.
T |
51,330 | Which statistical methods could I use to determine if a price is good, based on a history of prices? [closed] | Build a good ARIMA model for your price history incorporating memory, events (e.g. Black Friday etc ...), day-of-the-week, seasonal dummies, level shifts , local time trends and the run a program that incorporates robust ARIMA (not AIC or BIC based as those procedures assume no outliers and restrict the model form to a... | Which statistical methods could I use to determine if a price is good, based on a history of prices? | Build a good ARIMA model for your price history incorporating memory, events (e.g. Black Friday etc ...), day-of-the-week, seasonal dummies, level shifts , local time trends and the run a program that | Which statistical methods could I use to determine if a price is good, based on a history of prices? [closed]
Build a good ARIMA model for your price history incorporating memory, events (e.g. Black Friday etc ...), day-of-the-week, seasonal dummies, level shifts , local time trends and the run a program that incorpora... | Which statistical methods could I use to determine if a price is good, based on a history of prices?
Build a good ARIMA model for your price history incorporating memory, events (e.g. Black Friday etc ...), day-of-the-week, seasonal dummies, level shifts , local time trends and the run a program that |
51,331 | Which statistical methods could I use to determine if a price is good, based on a history of prices? [closed] | Clearly the answer depends on the dynamics of the prices, so having a model for that would be required, as a previous answer indicates. It seems to me that if one cannot assume anything about prices, this would be related to the secretary (or princess....) problem.
If all, say $r$, suitors of a princess are summoned s... | Which statistical methods could I use to determine if a price is good, based on a history of prices? | Clearly the answer depends on the dynamics of the prices, so having a model for that would be required, as a previous answer indicates. It seems to me that if one cannot assume anything about prices, | Which statistical methods could I use to determine if a price is good, based on a history of prices? [closed]
Clearly the answer depends on the dynamics of the prices, so having a model for that would be required, as a previous answer indicates. It seems to me that if one cannot assume anything about prices, this would... | Which statistical methods could I use to determine if a price is good, based on a history of prices?
Clearly the answer depends on the dynamics of the prices, so having a model for that would be required, as a previous answer indicates. It seems to me that if one cannot assume anything about prices, |
51,332 | Conditional vs. Unconditional Maximum Likelihood | The main reason for using conditional maximum likelihood is the resulting distribution. For Y|X ~N(x'B,Var(eps)) holds because the variation of Y only depends on the (normal) variation of the eps. As you know the correct assumption of the underlying density is a crucial point in MML estimation and hence, with unconditi... | Conditional vs. Unconditional Maximum Likelihood | The main reason for using conditional maximum likelihood is the resulting distribution. For Y|X ~N(x'B,Var(eps)) holds because the variation of Y only depends on the (normal) variation of the eps. As | Conditional vs. Unconditional Maximum Likelihood
The main reason for using conditional maximum likelihood is the resulting distribution. For Y|X ~N(x'B,Var(eps)) holds because the variation of Y only depends on the (normal) variation of the eps. As you know the correct assumption of the underlying density is a crucial ... | Conditional vs. Unconditional Maximum Likelihood
The main reason for using conditional maximum likelihood is the resulting distribution. For Y|X ~N(x'B,Var(eps)) holds because the variation of Y only depends on the (normal) variation of the eps. As |
51,333 | Bandits with mixed reward processes? | Wow, super super late coming back here. I just wanted to leave here that this question led to a published paper. Not having an answer was quite a good hint :)
https://ieeexplore.ieee.org/document/7390272 | Bandits with mixed reward processes? | Wow, super super late coming back here. I just wanted to leave here that this question led to a published paper. Not having an answer was quite a good hint :)
https://ieeexplore.ieee.org/document/7390 | Bandits with mixed reward processes?
Wow, super super late coming back here. I just wanted to leave here that this question led to a published paper. Not having an answer was quite a good hint :)
https://ieeexplore.ieee.org/document/7390272 | Bandits with mixed reward processes?
Wow, super super late coming back here. I just wanted to leave here that this question led to a published paper. Not having an answer was quite a good hint :)
https://ieeexplore.ieee.org/document/7390 |
51,334 | Should I control for random effects of participant in an individual differences design? | You're correct that the second model reduces the power to detect associations between the individual difference measure and the trial-level outcome by including a random intercept for each participant (including a random intercept is "what you are conceptually doing"). If that is the intention, the second model is spe... | Should I control for random effects of participant in an individual differences design? | You're correct that the second model reduces the power to detect associations between the individual difference measure and the trial-level outcome by including a random intercept for each participant | Should I control for random effects of participant in an individual differences design?
You're correct that the second model reduces the power to detect associations between the individual difference measure and the trial-level outcome by including a random intercept for each participant (including a random intercept i... | Should I control for random effects of participant in an individual differences design?
You're correct that the second model reduces the power to detect associations between the individual difference measure and the trial-level outcome by including a random intercept for each participant |
51,335 | Should I control for random effects of participant in an individual differences design? | As far as I know, random effects generally only affect variance that is not explained by the fixed effects. That is, the model first explains all the variance in the dependent variable with fixed effects and then accounts for the remaining residual variance by random effects. | Should I control for random effects of participant in an individual differences design? | As far as I know, random effects generally only affect variance that is not explained by the fixed effects. That is, the model first explains all the variance in the dependent variable with fixed effe | Should I control for random effects of participant in an individual differences design?
As far as I know, random effects generally only affect variance that is not explained by the fixed effects. That is, the model first explains all the variance in the dependent variable with fixed effects and then accounts for the re... | Should I control for random effects of participant in an individual differences design?
As far as I know, random effects generally only affect variance that is not explained by the fixed effects. That is, the model first explains all the variance in the dependent variable with fixed effe |
51,336 | Interpreting Granger causality test's results – i.e. $X = f(Y)$ | no. the null hypothesis would be: X does not granger cause Y or the other way.
Also, you accept or reject your null hypothesis depending on the level of significance.
if P value < Significance level, then Null hypothesis would be rejected.
if P value > Significance level, then Null hypothesis cannot be rejected. | Interpreting Granger causality test's results – i.e. $X = f(Y)$ | no. the null hypothesis would be: X does not granger cause Y or the other way.
Also, you accept or reject your null hypothesis depending on the level of significance.
if P value < Significance level, | Interpreting Granger causality test's results – i.e. $X = f(Y)$
no. the null hypothesis would be: X does not granger cause Y or the other way.
Also, you accept or reject your null hypothesis depending on the level of significance.
if P value < Significance level, then Null hypothesis would be rejected.
if P value > Sig... | Interpreting Granger causality test's results – i.e. $X = f(Y)$
no. the null hypothesis would be: X does not granger cause Y or the other way.
Also, you accept or reject your null hypothesis depending on the level of significance.
if P value < Significance level, |
51,337 | Interpreting Granger causality test's results – i.e. $X = f(Y)$ | For your specific example, as you are using F test the rejection of null hypothesis will be done as follows:
Compare obtained F_value to the F_critical, If F_value is greater than F_critical then you can reject the null hypothesis but before that you have to check P_value also. If P_value is less than Significance leve... | Interpreting Granger causality test's results – i.e. $X = f(Y)$ | For your specific example, as you are using F test the rejection of null hypothesis will be done as follows:
Compare obtained F_value to the F_critical, If F_value is greater than F_critical then you | Interpreting Granger causality test's results – i.e. $X = f(Y)$
For your specific example, as you are using F test the rejection of null hypothesis will be done as follows:
Compare obtained F_value to the F_critical, If F_value is greater than F_critical then you can reject the null hypothesis but before that you have ... | Interpreting Granger causality test's results – i.e. $X = f(Y)$
For your specific example, as you are using F test the rejection of null hypothesis will be done as follows:
Compare obtained F_value to the F_critical, If F_value is greater than F_critical then you |
51,338 | fit and cross-validate categorical sample data formed from observations [closed] | An possible algorithm would be to use a hash table with combinations of t1 and t2.
Sth i use frequently (and relatively simple) i using t1_t2 as a string key to a map of successes
Thus for your example you map would be sth like this:
Hash MAP:
Key Value (successes)
1_4 4
1_8 8
2_4 4
2_8 ... | fit and cross-validate categorical sample data formed from observations [closed] | An possible algorithm would be to use a hash table with combinations of t1 and t2.
Sth i use frequently (and relatively simple) i using t1_t2 as a string key to a map of successes
Thus for your exampl | fit and cross-validate categorical sample data formed from observations [closed]
An possible algorithm would be to use a hash table with combinations of t1 and t2.
Sth i use frequently (and relatively simple) i using t1_t2 as a string key to a map of successes
Thus for your example you map would be sth like this:
Hash ... | fit and cross-validate categorical sample data formed from observations [closed]
An possible algorithm would be to use a hash table with combinations of t1 and t2.
Sth i use frequently (and relatively simple) i using t1_t2 as a string key to a map of successes
Thus for your exampl |
51,339 | Testing for linear dependence among the columns of a matrix | You seem to ask a really provoking question: how to detect, given a singular correlation (or covariance, or sum-of-squares-and-cross-product) matrix, which column is linearly dependent on which. I tentatively suppose that sweep operation could help. Here is my probe in SPSS (not R) to illustrate.
Let's generate some da... | Testing for linear dependence among the columns of a matrix | You seem to ask a really provoking question: how to detect, given a singular correlation (or covariance, or sum-of-squares-and-cross-product) matrix, which column is linearly dependent on which. I ten | Testing for linear dependence among the columns of a matrix
You seem to ask a really provoking question: how to detect, given a singular correlation (or covariance, or sum-of-squares-and-cross-product) matrix, which column is linearly dependent on which. I tentatively suppose that sweep operation could help. Here is my... | Testing for linear dependence among the columns of a matrix
You seem to ask a really provoking question: how to detect, given a singular correlation (or covariance, or sum-of-squares-and-cross-product) matrix, which column is linearly dependent on which. I ten |
51,340 | Testing for linear dependence among the columns of a matrix | Here's a straightforward approach: compute the rank of the matrix that results from removing each of the columns. The columns which, when removed, result in the highest rank are the linearly dependent ones (since removing those does not decrease rank, while removing a linearly independent column does).
In R:
rankifrem... | Testing for linear dependence among the columns of a matrix | Here's a straightforward approach: compute the rank of the matrix that results from removing each of the columns. The columns which, when removed, result in the highest rank are the linearly dependen | Testing for linear dependence among the columns of a matrix
Here's a straightforward approach: compute the rank of the matrix that results from removing each of the columns. The columns which, when removed, result in the highest rank are the linearly dependent ones (since removing those does not decrease rank, while r... | Testing for linear dependence among the columns of a matrix
Here's a straightforward approach: compute the rank of the matrix that results from removing each of the columns. The columns which, when removed, result in the highest rank are the linearly dependen |
51,341 | Testing for linear dependence among the columns of a matrix | The question asks about "identifying underlying [linear] relationships" among variables.
The quick and easy way to detect relationships is to regress any other variable (use a constant, even) against those variables using your favorite software: any good regression procedure will detect and diagnose collinearity. (You... | Testing for linear dependence among the columns of a matrix | The question asks about "identifying underlying [linear] relationships" among variables.
The quick and easy way to detect relationships is to regress any other variable (use a constant, even) against | Testing for linear dependence among the columns of a matrix
The question asks about "identifying underlying [linear] relationships" among variables.
The quick and easy way to detect relationships is to regress any other variable (use a constant, even) against those variables using your favorite software: any good regre... | Testing for linear dependence among the columns of a matrix
The question asks about "identifying underlying [linear] relationships" among variables.
The quick and easy way to detect relationships is to regress any other variable (use a constant, even) against |
51,342 | Testing for linear dependence among the columns of a matrix | What I'd try to do here for diagnostic purposes is to take the $502\times 480$ matrix (that is, the transpose) and determine the singular values of the matrix (for diagnostic purposes, you don't need the full singular value decomposition... yet). Once you have the 480 singular values, check how many of those are "small... | Testing for linear dependence among the columns of a matrix | What I'd try to do here for diagnostic purposes is to take the $502\times 480$ matrix (that is, the transpose) and determine the singular values of the matrix (for diagnostic purposes, you don't need | Testing for linear dependence among the columns of a matrix
What I'd try to do here for diagnostic purposes is to take the $502\times 480$ matrix (that is, the transpose) and determine the singular values of the matrix (for diagnostic purposes, you don't need the full singular value decomposition... yet). Once you have... | Testing for linear dependence among the columns of a matrix
What I'd try to do here for diagnostic purposes is to take the $502\times 480$ matrix (that is, the transpose) and determine the singular values of the matrix (for diagnostic purposes, you don't need |
51,343 | Testing for linear dependence among the columns of a matrix | Not that the answer @Whuber gave really needs to be expanded on but I thought I'd provide a brief description of the math.
If the linear combination $\mathbf{X'Xv}=\mathbf{0}$ for $\mathbf{v}\neq\mathbf{0}$ then $\mathbf{v}$ is an eigenvector of $\mathbf{X'X}$ associated with eigenvalue $\lambda=0$. The eigenvectors an... | Testing for linear dependence among the columns of a matrix | Not that the answer @Whuber gave really needs to be expanded on but I thought I'd provide a brief description of the math.
If the linear combination $\mathbf{X'Xv}=\mathbf{0}$ for $\mathbf{v}\neq\math | Testing for linear dependence among the columns of a matrix
Not that the answer @Whuber gave really needs to be expanded on but I thought I'd provide a brief description of the math.
If the linear combination $\mathbf{X'Xv}=\mathbf{0}$ for $\mathbf{v}\neq\mathbf{0}$ then $\mathbf{v}$ is an eigenvector of $\mathbf{X'X}$... | Testing for linear dependence among the columns of a matrix
Not that the answer @Whuber gave really needs to be expanded on but I thought I'd provide a brief description of the math.
If the linear combination $\mathbf{X'Xv}=\mathbf{0}$ for $\mathbf{v}\neq\math |
51,344 | Testing for linear dependence among the columns of a matrix | I ran into this issue roughly two weeks ago and decided that I needed to revisit it because when dealing with massive data sets, it is impossible to do these things manually.
I created a for() loop that calculates the rank of the matrix one column at a time. So for the first iteration, the rank will be 1. The second, ... | Testing for linear dependence among the columns of a matrix | I ran into this issue roughly two weeks ago and decided that I needed to revisit it because when dealing with massive data sets, it is impossible to do these things manually.
I created a for() loop t | Testing for linear dependence among the columns of a matrix
I ran into this issue roughly two weeks ago and decided that I needed to revisit it because when dealing with massive data sets, it is impossible to do these things manually.
I created a for() loop that calculates the rank of the matrix one column at a time. ... | Testing for linear dependence among the columns of a matrix
I ran into this issue roughly two weeks ago and decided that I needed to revisit it because when dealing with massive data sets, it is impossible to do these things manually.
I created a for() loop t |
51,345 | Testing for linear dependence among the columns of a matrix | Rank, r of a matrix = number of linearly independent columns (or rows) of a matrix. For a n by n matrix A, rank(A) = n => all columns (or rows) are linearly independent. | Testing for linear dependence among the columns of a matrix | Rank, r of a matrix = number of linearly independent columns (or rows) of a matrix. For a n by n matrix A, rank(A) = n => all columns (or rows) are linearly independent. | Testing for linear dependence among the columns of a matrix
Rank, r of a matrix = number of linearly independent columns (or rows) of a matrix. For a n by n matrix A, rank(A) = n => all columns (or rows) are linearly independent. | Testing for linear dependence among the columns of a matrix
Rank, r of a matrix = number of linearly independent columns (or rows) of a matrix. For a n by n matrix A, rank(A) = n => all columns (or rows) are linearly independent. |
51,346 | Needle-in-a-haystack Regularized Regression | Ultimately, I ended up abandoning regularized approaches, as they are simply too biased for unbalanced categorical/binary features. I've actually grown quite skeptical of regularization in general, at least in my problem domain.
Instead, I went with OLS using stepwise feature selection with k-fold cross-validation. I ... | Needle-in-a-haystack Regularized Regression | Ultimately, I ended up abandoning regularized approaches, as they are simply too biased for unbalanced categorical/binary features. I've actually grown quite skeptical of regularization in general, at | Needle-in-a-haystack Regularized Regression
Ultimately, I ended up abandoning regularized approaches, as they are simply too biased for unbalanced categorical/binary features. I've actually grown quite skeptical of regularization in general, at least in my problem domain.
Instead, I went with OLS using stepwise featur... | Needle-in-a-haystack Regularized Regression
Ultimately, I ended up abandoning regularized approaches, as they are simply too biased for unbalanced categorical/binary features. I've actually grown quite skeptical of regularization in general, at |
51,347 | Confidence interval of RMSE | I might be able to give an answer to your question under certain conditions.
Let $x_{i}$ be your true value for the $i^{th}$ data point and $\hat{x}_{i}$ the estimated value. If we assume that the differences between the estimated and true values have
mean zero (i.e. the $\hat{x}_{i}$ are distributed around $x_{i}$)
... | Confidence interval of RMSE | I might be able to give an answer to your question under certain conditions.
Let $x_{i}$ be your true value for the $i^{th}$ data point and $\hat{x}_{i}$ the estimated value. If we assume that the dif | Confidence interval of RMSE
I might be able to give an answer to your question under certain conditions.
Let $x_{i}$ be your true value for the $i^{th}$ data point and $\hat{x}_{i}$ the estimated value. If we assume that the differences between the estimated and true values have
mean zero (i.e. the $\hat{x}_{i}$ are d... | Confidence interval of RMSE
I might be able to give an answer to your question under certain conditions.
Let $x_{i}$ be your true value for the $i^{th}$ data point and $\hat{x}_{i}$ the estimated value. If we assume that the dif |
51,348 | Confidence interval of RMSE | The reasoning in the answer by fabee seems correct if applied to the STDE (standard deviation of the error), not the RMSE.
Using similar nomenclature, $i=1,\,\ldots,\,n$ is an index representing each record of data, $x_i$ is the true value and $\hat{x}_i$ is a measurement or prediction.
The error $\epsilon_i$, BIAS, MS... | Confidence interval of RMSE | The reasoning in the answer by fabee seems correct if applied to the STDE (standard deviation of the error), not the RMSE.
Using similar nomenclature, $i=1,\,\ldots,\,n$ is an index representing each | Confidence interval of RMSE
The reasoning in the answer by fabee seems correct if applied to the STDE (standard deviation of the error), not the RMSE.
Using similar nomenclature, $i=1,\,\ldots,\,n$ is an index representing each record of data, $x_i$ is the true value and $\hat{x}_i$ is a measurement or prediction.
The ... | Confidence interval of RMSE
The reasoning in the answer by fabee seems correct if applied to the STDE (standard deviation of the error), not the RMSE.
Using similar nomenclature, $i=1,\,\ldots,\,n$ is an index representing each |
51,349 | Confidence interval of RMSE | Following Faaber 1999, the uncertainty of RMSE is given as
$$\sigma (\hat{RMSE})/RMSE = \sqrt{\frac{1}{2n}}$$
where $n$ is the number of datapoints. | Confidence interval of RMSE | Following Faaber 1999, the uncertainty of RMSE is given as
$$\sigma (\hat{RMSE})/RMSE = \sqrt{\frac{1}{2n}}$$
where $n$ is the number of datapoints. | Confidence interval of RMSE
Following Faaber 1999, the uncertainty of RMSE is given as
$$\sigma (\hat{RMSE})/RMSE = \sqrt{\frac{1}{2n}}$$
where $n$ is the number of datapoints. | Confidence interval of RMSE
Following Faaber 1999, the uncertainty of RMSE is given as
$$\sigma (\hat{RMSE})/RMSE = \sqrt{\frac{1}{2n}}$$
where $n$ is the number of datapoints. |
51,350 | Confidence interval of RMSE | Borrowing code from @Bryan Shalloway's link (https://gist.github.com/brshallo/7eed49c743ac165ced2294a70e73e65e, which is in the comment in the accepted answer), you can calculate this in R with the RMSE value and the degrees of freedom, which @fabee suggests is n (not n-1) in this case.
The R function:
rmse_interval <... | Confidence interval of RMSE | Borrowing code from @Bryan Shalloway's link (https://gist.github.com/brshallo/7eed49c743ac165ced2294a70e73e65e, which is in the comment in the accepted answer), you can calculate this in R with the RM | Confidence interval of RMSE
Borrowing code from @Bryan Shalloway's link (https://gist.github.com/brshallo/7eed49c743ac165ced2294a70e73e65e, which is in the comment in the accepted answer), you can calculate this in R with the RMSE value and the degrees of freedom, which @fabee suggests is n (not n-1) in this case.
The... | Confidence interval of RMSE
Borrowing code from @Bryan Shalloway's link (https://gist.github.com/brshallo/7eed49c743ac165ced2294a70e73e65e, which is in the comment in the accepted answer), you can calculate this in R with the RM |
51,351 | Coding for an ordered covariate | You could check out Gertheiss & Tutz, Penalized Regression with Ordinal Predictors, & their R package ordPens.
They say:–
Rather than estimating the parameters by simple maximum likelihood methods we propose to penalize differences between coefficients of adjacent categories
in the estimation procedure. The rational... | Coding for an ordered covariate | You could check out Gertheiss & Tutz, Penalized Regression with Ordinal Predictors, & their R package ordPens.
They say:–
Rather than estimating the parameters by simple maximum likelihood methods we | Coding for an ordered covariate
You could check out Gertheiss & Tutz, Penalized Regression with Ordinal Predictors, & their R package ordPens.
They say:–
Rather than estimating the parameters by simple maximum likelihood methods we propose to penalize differences between coefficients of adjacent categories
in the es... | Coding for an ordered covariate
You could check out Gertheiss & Tutz, Penalized Regression with Ordinal Predictors, & their R package ordPens.
They say:–
Rather than estimating the parameters by simple maximum likelihood methods we |
51,352 | What does it imply when an estimate is not inside its 95% confidence interval? [closed] | Keep in mind that a 95% confidence interval will NOT contain the true parameter value 5% of the time if all of the assumptions are valid. Either your model is valid and you are experiencing the 5%, or your model is invalid and you need to check the assumptions. | What does it imply when an estimate is not inside its 95% confidence interval? [closed] | Keep in mind that a 95% confidence interval will NOT contain the true parameter value 5% of the time if all of the assumptions are valid. Either your model is valid and you are experiencing the 5%, or | What does it imply when an estimate is not inside its 95% confidence interval? [closed]
Keep in mind that a 95% confidence interval will NOT contain the true parameter value 5% of the time if all of the assumptions are valid. Either your model is valid and you are experiencing the 5%, or your model is invalid and you n... | What does it imply when an estimate is not inside its 95% confidence interval? [closed]
Keep in mind that a 95% confidence interval will NOT contain the true parameter value 5% of the time if all of the assumptions are valid. Either your model is valid and you are experiencing the 5%, or |
51,353 | Question about classification with hidden Markov models using depmixS4 [closed] | You must use forward function to calculate the likelihood by sum forward's probability from first step until last one.
sum(forward_prob(t)) | Question about classification with hidden Markov models using depmixS4 [closed] | You must use forward function to calculate the likelihood by sum forward's probability from first step until last one.
sum(forward_prob(t)) | Question about classification with hidden Markov models using depmixS4 [closed]
You must use forward function to calculate the likelihood by sum forward's probability from first step until last one.
sum(forward_prob(t)) | Question about classification with hidden Markov models using depmixS4 [closed]
You must use forward function to calculate the likelihood by sum forward's probability from first step until last one.
sum(forward_prob(t)) |
51,354 | Principled way of collapsing categorical variables with many levels? | If I understood correctly, you imagine a linear model where one of the predictors is categorical (e.g. college major); and you expect that for some subgroups of its levels (subgroups of categories) the coefficients might be exactly the same. So perhaps the regression coefficients for Maths and Physics are the same, but... | Principled way of collapsing categorical variables with many levels? | If I understood correctly, you imagine a linear model where one of the predictors is categorical (e.g. college major); and you expect that for some subgroups of its levels (subgroups of categories) th | Principled way of collapsing categorical variables with many levels?
If I understood correctly, you imagine a linear model where one of the predictors is categorical (e.g. college major); and you expect that for some subgroups of its levels (subgroups of categories) the coefficients might be exactly the same. So perhap... | Principled way of collapsing categorical variables with many levels?
If I understood correctly, you imagine a linear model where one of the predictors is categorical (e.g. college major); and you expect that for some subgroups of its levels (subgroups of categories) th |
51,355 | Principled way of collapsing categorical variables with many levels? | I've wrestled with this on a project I've been working on, and at this point I've decided there really isn't a good way to fuse categories and so I'm trying a hierarchical/mixed-effects model where my equivalent of your major is a random effect.
Also, in situations like this there seem to actually be two fusing decisio... | Principled way of collapsing categorical variables with many levels? | I've wrestled with this on a project I've been working on, and at this point I've decided there really isn't a good way to fuse categories and so I'm trying a hierarchical/mixed-effects model where my | Principled way of collapsing categorical variables with many levels?
I've wrestled with this on a project I've been working on, and at this point I've decided there really isn't a good way to fuse categories and so I'm trying a hierarchical/mixed-effects model where my equivalent of your major is a random effect.
Also,... | Principled way of collapsing categorical variables with many levels?
I've wrestled with this on a project I've been working on, and at this point I've decided there really isn't a good way to fuse categories and so I'm trying a hierarchical/mixed-effects model where my |
51,356 | Principled way of collapsing categorical variables with many levels? | If you have an auxiliary independent variable that is logical to use as an anchor for the categorical predictor, consider the use of Fisher's optimum scoring algorithm, which is related to his linear discriminant analysis. Suppose that you wanted to map the college major into a single continuous metric, and suppose th... | Principled way of collapsing categorical variables with many levels? | If you have an auxiliary independent variable that is logical to use as an anchor for the categorical predictor, consider the use of Fisher's optimum scoring algorithm, which is related to his linear | Principled way of collapsing categorical variables with many levels?
If you have an auxiliary independent variable that is logical to use as an anchor for the categorical predictor, consider the use of Fisher's optimum scoring algorithm, which is related to his linear discriminant analysis. Suppose that you wanted to ... | Principled way of collapsing categorical variables with many levels?
If you have an auxiliary independent variable that is logical to use as an anchor for the categorical predictor, consider the use of Fisher's optimum scoring algorithm, which is related to his linear |
51,357 | Principled way of collapsing categorical variables with many levels? | One way to handle this situation is to recode the categorical variable into a continuous one, using what is known as "target coding" (aka "impact coding") [1]. Let $Z$ be an input variable with categorical levels ${z^1, ..., z^K }$, and let $Y$ be the output/target/response variable. Replace $Z$ with $\operatorname{Imp... | Principled way of collapsing categorical variables with many levels? | One way to handle this situation is to recode the categorical variable into a continuous one, using what is known as "target coding" (aka "impact coding") [1]. Let $Z$ be an input variable with catego | Principled way of collapsing categorical variables with many levels?
One way to handle this situation is to recode the categorical variable into a continuous one, using what is known as "target coding" (aka "impact coding") [1]. Let $Z$ be an input variable with categorical levels ${z^1, ..., z^K }$, and let $Y$ be the... | Principled way of collapsing categorical variables with many levels?
One way to handle this situation is to recode the categorical variable into a continuous one, using what is known as "target coding" (aka "impact coding") [1]. Let $Z$ be an input variable with catego |
51,358 | Principled way of collapsing categorical variables with many levels? | There are multiple questions here, and some of them are asked & answered earlier. If the problem is computation taking a long time: There are multiple methods to deal with that, see large scale regression with sparse feature matrix and the paper by Maechler and Bates.
But it might well be that the problem is with ... | Principled way of collapsing categorical variables with many levels? | There are multiple questions here, and some of them are asked & answered earlier. If the problem is computation taking a long time: There are multiple methods to deal with that, see large scale regr | Principled way of collapsing categorical variables with many levels?
There are multiple questions here, and some of them are asked & answered earlier. If the problem is computation taking a long time: There are multiple methods to deal with that, see large scale regression with sparse feature matrix and the paper by... | Principled way of collapsing categorical variables with many levels?
There are multiple questions here, and some of them are asked & answered earlier. If the problem is computation taking a long time: There are multiple methods to deal with that, see large scale regr |
51,359 | Principled way of collapsing categorical variables with many levels? | The paper "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems" leverages hierarchical structure in the category attributes in a nested 'empirical Bayes' scheme at every pool/level to map the categorical variable into a posterior class probability, which can be u... | Principled way of collapsing categorical variables with many levels? | The paper "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems" leverages hierarchical structure in the category attributes in a nested 'empiri | Principled way of collapsing categorical variables with many levels?
The paper "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems" leverages hierarchical structure in the category attributes in a nested 'empirical Bayes' scheme at every pool/level to map the ca... | Principled way of collapsing categorical variables with many levels?
The paper "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems" leverages hierarchical structure in the category attributes in a nested 'empiri |
51,360 | Optimizing matching of players in a tournament round | I haven't found an algorithm that guarantees the highest-total-value for pairings selections, but I have found one that guarantees the lowest-total-value, so we can modify the original matrix to accommodate that algorithm. Basically, we just have to subtract all of the match quality scores from 1.0 and assign a value o... | Optimizing matching of players in a tournament round | I haven't found an algorithm that guarantees the highest-total-value for pairings selections, but I have found one that guarantees the lowest-total-value, so we can modify the original matrix to accom | Optimizing matching of players in a tournament round
I haven't found an algorithm that guarantees the highest-total-value for pairings selections, but I have found one that guarantees the lowest-total-value, so we can modify the original matrix to accommodate that algorithm. Basically, we just have to subtract all of t... | Optimizing matching of players in a tournament round
I haven't found an algorithm that guarantees the highest-total-value for pairings selections, but I have found one that guarantees the lowest-total-value, so we can modify the original matrix to accom |
51,361 | Central limit theorem with unknown variance | Perhaps you can bound your variance. Suppose, for example, that you know your data must be in the range $[a,b]$. Then Popoviciu's inequality bounds your variance by $\sigma^2 \le (1/4)(b-a)^2$. Using the upper bound in the formulas you found will be a bit of overkill, but it should satisfy your requirements. | Central limit theorem with unknown variance | Perhaps you can bound your variance. Suppose, for example, that you know your data must be in the range $[a,b]$. Then Popoviciu's inequality bounds your variance by $\sigma^2 \le (1/4)(b-a)^2$. Using | Central limit theorem with unknown variance
Perhaps you can bound your variance. Suppose, for example, that you know your data must be in the range $[a,b]$. Then Popoviciu's inequality bounds your variance by $\sigma^2 \le (1/4)(b-a)^2$. Using the upper bound in the formulas you found will be a bit of overkill, but it ... | Central limit theorem with unknown variance
Perhaps you can bound your variance. Suppose, for example, that you know your data must be in the range $[a,b]$. Then Popoviciu's inequality bounds your variance by $\sigma^2 \le (1/4)(b-a)^2$. Using |
51,362 | Central limit theorem with unknown variance | CTL is all about independent and identically distributed (i.i.d.) random variables, with finite mean and variance. I edit the answer just to add that you don't have to know your parameter , but be sure that this parameter is finite and identical along your runs.
In order to estimate the parameter mean with unknownk va... | Central limit theorem with unknown variance | CTL is all about independent and identically distributed (i.i.d.) random variables, with finite mean and variance. I edit the answer just to add that you don't have to know your parameter , but be sur | Central limit theorem with unknown variance
CTL is all about independent and identically distributed (i.i.d.) random variables, with finite mean and variance. I edit the answer just to add that you don't have to know your parameter , but be sure that this parameter is finite and identical along your runs.
In order to ... | Central limit theorem with unknown variance
CTL is all about independent and identically distributed (i.i.d.) random variables, with finite mean and variance. I edit the answer just to add that you don't have to know your parameter , but be sur |
51,363 | Quantile regression and heteroscedasticity/autocorrelation | Quantile regression does not assume that the error terms are normally distributed (nor does it assume some other shape for them). There are tons of references for this, e.g. Koenker 2005. However, while I haven't found anywhere a statement that quantile regression assumes independence, several papers e.g. Ceraci and Bo... | Quantile regression and heteroscedasticity/autocorrelation | Quantile regression does not assume that the error terms are normally distributed (nor does it assume some other shape for them). There are tons of references for this, e.g. Koenker 2005. However, whi | Quantile regression and heteroscedasticity/autocorrelation
Quantile regression does not assume that the error terms are normally distributed (nor does it assume some other shape for them). There are tons of references for this, e.g. Koenker 2005. However, while I haven't found anywhere a statement that quantile regress... | Quantile regression and heteroscedasticity/autocorrelation
Quantile regression does not assume that the error terms are normally distributed (nor does it assume some other shape for them). There are tons of references for this, e.g. Koenker 2005. However, whi |
51,364 | Quantile regression and heteroscedasticity/autocorrelation | The asymptotic results come from the general theory of extremum estimators which allows for the complications you bring up. However, the criterion function in this case is not smooth, so there might be issues with estimation of the derivatives, while the bootstrap is still reliable. Some modifications allow to speed it... | Quantile regression and heteroscedasticity/autocorrelation | The asymptotic results come from the general theory of extremum estimators which allows for the complications you bring up. However, the criterion function in this case is not smooth, so there might b | Quantile regression and heteroscedasticity/autocorrelation
The asymptotic results come from the general theory of extremum estimators which allows for the complications you bring up. However, the criterion function in this case is not smooth, so there might be issues with estimation of the derivatives, while the bootst... | Quantile regression and heteroscedasticity/autocorrelation
The asymptotic results come from the general theory of extremum estimators which allows for the complications you bring up. However, the criterion function in this case is not smooth, so there might b |
51,365 | R's lmer cheat sheet | What's the difference between (~1 +....) and (1 | ...) and (0 | ...) etc.?
Say you have variable V1 predicted by categorical variable V2, which is treated as a random effect, and continuous variable V3, which is treated as a linear fixed effect. Using lmer syntax, simplest model (M1) is:
V1 ~ (1|V2) + V3
This model w... | R's lmer cheat sheet | What's the difference between (~1 +....) and (1 | ...) and (0 | ...) etc.?
Say you have variable V1 predicted by categorical variable V2, which is treated as a random effect, and continuous variable | R's lmer cheat sheet
What's the difference between (~1 +....) and (1 | ...) and (0 | ...) etc.?
Say you have variable V1 predicted by categorical variable V2, which is treated as a random effect, and continuous variable V3, which is treated as a linear fixed effect. Using lmer syntax, simplest model (M1) is:
V1 ~ (1|V... | R's lmer cheat sheet
What's the difference between (~1 +....) and (1 | ...) and (0 | ...) etc.?
Say you have variable V1 predicted by categorical variable V2, which is treated as a random effect, and continuous variable |
51,366 | R's lmer cheat sheet | The general trick is, as mentioned in another answer, is that the formula follows the form dependent ~ independent | grouping. The groupingis generally a random factor, you can include fixed factors without any grouping and you can have additional random factors without any fixed factor (an intercept-only model). A + ... | R's lmer cheat sheet | The general trick is, as mentioned in another answer, is that the formula follows the form dependent ~ independent | grouping. The groupingis generally a random factor, you can include fixed factors w | R's lmer cheat sheet
The general trick is, as mentioned in another answer, is that the formula follows the form dependent ~ independent | grouping. The groupingis generally a random factor, you can include fixed factors without any grouping and you can have additional random factors without any fixed factor (an interce... | R's lmer cheat sheet
The general trick is, as mentioned in another answer, is that the formula follows the form dependent ~ independent | grouping. The groupingis generally a random factor, you can include fixed factors w |
51,367 | R's lmer cheat sheet | The | symbol indicates a grouping factor in mixed methods.
As per Pinheiro & Bates:
...The formula also designates a response and, when available, a primary covariate. It is given as
response ~ primary | grouping
where response is an expression for the response, primary is an expression for the primary covariate, and... | R's lmer cheat sheet | The | symbol indicates a grouping factor in mixed methods.
As per Pinheiro & Bates:
...The formula also designates a response and, when available, a primary covariate. It is given as
response ~ prima | R's lmer cheat sheet
The | symbol indicates a grouping factor in mixed methods.
As per Pinheiro & Bates:
...The formula also designates a response and, when available, a primary covariate. It is given as
response ~ primary | grouping
where response is an expression for the response, primary is an expression for the p... | R's lmer cheat sheet
The | symbol indicates a grouping factor in mixed methods.
As per Pinheiro & Bates:
...The formula also designates a response and, when available, a primary covariate. It is given as
response ~ prima |
51,368 | Is there an easy way to calculate significant difference between two largely overlapping correlations from same sample? | You could fit a regression model with all three measures as predictors, then fit a new regression model with 1 or 2 dropped out and do a full-reduced model test to see if there is a significant difference in the models. This answers the question of "do the variables in the full, but not reduced, model contribute signi... | Is there an easy way to calculate significant difference between two largely overlapping correlation | You could fit a regression model with all three measures as predictors, then fit a new regression model with 1 or 2 dropped out and do a full-reduced model test to see if there is a significant differ | Is there an easy way to calculate significant difference between two largely overlapping correlations from same sample?
You could fit a regression model with all three measures as predictors, then fit a new regression model with 1 or 2 dropped out and do a full-reduced model test to see if there is a significant differ... | Is there an easy way to calculate significant difference between two largely overlapping correlation
You could fit a regression model with all three measures as predictors, then fit a new regression model with 1 or 2 dropped out and do a full-reduced model test to see if there is a significant differ |
51,369 | Is there an easy way to calculate significant difference between two largely overlapping correlations from same sample? | I thought you were talking about less than 100. 4000 may be barely enough. but a difference of 0.02 is not very meaningful. | Is there an easy way to calculate significant difference between two largely overlapping correlation | I thought you were talking about less than 100. 4000 may be barely enough. but a difference of 0.02 is not very meaningful. | Is there an easy way to calculate significant difference between two largely overlapping correlations from same sample?
I thought you were talking about less than 100. 4000 may be barely enough. but a difference of 0.02 is not very meaningful. | Is there an easy way to calculate significant difference between two largely overlapping correlation
I thought you were talking about less than 100. 4000 may be barely enough. but a difference of 0.02 is not very meaningful. |
51,370 | Sequential clustering algorithm | Constrained clustering maintains data order. There is a package in R called 'rioja' that implements this in the function 'chclust'.
The procedure isn't too complex though:
Calculate inter-point distance
Find the smallest distance between adjacent points
Average the value of the two points to generate a single value
S... | Sequential clustering algorithm | Constrained clustering maintains data order. There is a package in R called 'rioja' that implements this in the function 'chclust'.
The procedure isn't too complex though:
Calculate inter-point dist | Sequential clustering algorithm
Constrained clustering maintains data order. There is a package in R called 'rioja' that implements this in the function 'chclust'.
The procedure isn't too complex though:
Calculate inter-point distance
Find the smallest distance between adjacent points
Average the value of the two poi... | Sequential clustering algorithm
Constrained clustering maintains data order. There is a package in R called 'rioja' that implements this in the function 'chclust'.
The procedure isn't too complex though:
Calculate inter-point dist |
51,371 | What do the terms "nearly-optimal rate", "near-minimax rate", "minimax optimal rate" and "minimax rate" mean in the context of posterior consistency? | Can't comment due to lack of rep, but think I may be able to add some useful input if you haven't figured it out already.
I believe what you are missing here is that the speed of convergence of the mass of the posterior on the true value is an important factor and that this is likely what is being referred to as the ra... | What do the terms "nearly-optimal rate", "near-minimax rate", "minimax optimal rate" and "minimax ra | Can't comment due to lack of rep, but think I may be able to add some useful input if you haven't figured it out already.
I believe what you are missing here is that the speed of convergence of the ma | What do the terms "nearly-optimal rate", "near-minimax rate", "minimax optimal rate" and "minimax rate" mean in the context of posterior consistency?
Can't comment due to lack of rep, but think I may be able to add some useful input if you haven't figured it out already.
I believe what you are missing here is that the ... | What do the terms "nearly-optimal rate", "near-minimax rate", "minimax optimal rate" and "minimax ra
Can't comment due to lack of rep, but think I may be able to add some useful input if you haven't figured it out already.
I believe what you are missing here is that the speed of convergence of the ma |
51,372 | Parameter identification v. causal identification | Let's take the simple linear model and discuss three different definitions of the parameter of interest in the linear model, with very different identification settings. All three scenarios are very common in empirical work in economics and it's typically only clear from context of them characterise the analysis.
Here'... | Parameter identification v. causal identification | Let's take the simple linear model and discuss three different definitions of the parameter of interest in the linear model, with very different identification settings. All three scenarios are very c | Parameter identification v. causal identification
Let's take the simple linear model and discuss three different definitions of the parameter of interest in the linear model, with very different identification settings. All three scenarios are very common in empirical work in economics and it's typically only clear fro... | Parameter identification v. causal identification
Let's take the simple linear model and discuss three different definitions of the parameter of interest in the linear model, with very different identification settings. All three scenarios are very c |
51,373 | Parameter identification v. causal identification | I think that the way you explained your two different takes on "identification" was clear but I'm not familiar with your second take.
My understanding ( I wouldn't even call myself a novice in this. I'd call myself super novice ) is that the notion you use in your second description, "identification strategy", is the ... | Parameter identification v. causal identification | I think that the way you explained your two different takes on "identification" was clear but I'm not familiar with your second take.
My understanding ( I wouldn't even call myself a novice in this. I | Parameter identification v. causal identification
I think that the way you explained your two different takes on "identification" was clear but I'm not familiar with your second take.
My understanding ( I wouldn't even call myself a novice in this. I'd call myself super novice ) is that the notion you use in your secon... | Parameter identification v. causal identification
I think that the way you explained your two different takes on "identification" was clear but I'm not familiar with your second take.
My understanding ( I wouldn't even call myself a novice in this. I |
51,374 | Parameter identification v. causal identification | https://autobox.com/pdfs/PREFERRED.pdf presents single equation- multivariable procedures to identify a useful model. http://www.autobox.com/pdfs/WHY-WE-FILTER.ppt also sheds some light on model identification. Parameter identification can lead to revising tentative model identification via necessity and sufficiency ch... | Parameter identification v. causal identification | https://autobox.com/pdfs/PREFERRED.pdf presents single equation- multivariable procedures to identify a useful model. http://www.autobox.com/pdfs/WHY-WE-FILTER.ppt also sheds some light on model ident | Parameter identification v. causal identification
https://autobox.com/pdfs/PREFERRED.pdf presents single equation- multivariable procedures to identify a useful model. http://www.autobox.com/pdfs/WHY-WE-FILTER.ppt also sheds some light on model identification. Parameter identification can lead to revising tentative mod... | Parameter identification v. causal identification
https://autobox.com/pdfs/PREFERRED.pdf presents single equation- multivariable procedures to identify a useful model. http://www.autobox.com/pdfs/WHY-WE-FILTER.ppt also sheds some light on model ident |
51,375 | What's the state of the art for time series forecasting in 2019? [duplicate] | How to predict the next number in a series while having additional series of data that might affect it? lays out the arguements for pursuing ARMAX models when you have 1 endogenous time series. For cases where you have more than one consider following VECTOR ARIMA threads. | What's the state of the art for time series forecasting in 2019? [duplicate] | How to predict the next number in a series while having additional series of data that might affect it? lays out the arguements for pursuing ARMAX models when you have 1 endogenous time series. For ca | What's the state of the art for time series forecasting in 2019? [duplicate]
How to predict the next number in a series while having additional series of data that might affect it? lays out the arguements for pursuing ARMAX models when you have 1 endogenous time series. For cases where you have more than one consider f... | What's the state of the art for time series forecasting in 2019? [duplicate]
How to predict the next number in a series while having additional series of data that might affect it? lays out the arguements for pursuing ARMAX models when you have 1 endogenous time series. For ca |
51,376 | Modelling Time Series of Ratios | Some advice from the font of all time series knowledge ( grin ! )
when you convert two column to one column ... you lose information thus dp not do this
model y as a function of x .. predict x to obtain a prediction of y and then if you are so inclined compute the ratio of the predicted y to the predicted x to obtain ... | Modelling Time Series of Ratios | Some advice from the font of all time series knowledge ( grin ! )
when you convert two column to one column ... you lose information thus dp not do this
model y as a function of x .. predict x to obt | Modelling Time Series of Ratios
Some advice from the font of all time series knowledge ( grin ! )
when you convert two column to one column ... you lose information thus dp not do this
model y as a function of x .. predict x to obtain a prediction of y and then if you are so inclined compute the ratio of the predicted... | Modelling Time Series of Ratios
Some advice from the font of all time series knowledge ( grin ! )
when you convert two column to one column ... you lose information thus dp not do this
model y as a function of x .. predict x to obt |
51,377 | How do I find multiple change points in an online dataset? | Please find the description of the algorithm called SaRa here. It can be modified and used as an "online" version of Circular-Binary Segmentation algorithm.
HMM can be modified for your purposes (3 states: normal, above and below, after change point the state is switching to normal again, finding its location according... | How do I find multiple change points in an online dataset? | Please find the description of the algorithm called SaRa here. It can be modified and used as an "online" version of Circular-Binary Segmentation algorithm.
HMM can be modified for your purposes (3 st | How do I find multiple change points in an online dataset?
Please find the description of the algorithm called SaRa here. It can be modified and used as an "online" version of Circular-Binary Segmentation algorithm.
HMM can be modified for your purposes (3 states: normal, above and below, after change point the state i... | How do I find multiple change points in an online dataset?
Please find the description of the algorithm called SaRa here. It can be modified and used as an "online" version of Circular-Binary Segmentation algorithm.
HMM can be modified for your purposes (3 st |
51,378 | How do I find multiple change points in an online dataset? | 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.
This seems to be a classical anomaly detection questio... | How do I find multiple change points in an online dataset? | 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 do I find multiple change points in an online dataset?
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 do I find multiple change points in an online dataset?
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.
|
51,379 | R: statistical test to identify samples with too high variability | I just looked at this.
My approach was:
compute the mean, standard deviation, and count for each set of samples
compute the critical t-threshold given alpha, the sample size, and the nature of the fit (quadratic). I was using excel so I used "T.inv".
transform the data by subtracting the mean, then dividing by the st... | R: statistical test to identify samples with too high variability | I just looked at this.
My approach was:
compute the mean, standard deviation, and count for each set of samples
compute the critical t-threshold given alpha, the sample size, and the nature of the fi | R: statistical test to identify samples with too high variability
I just looked at this.
My approach was:
compute the mean, standard deviation, and count for each set of samples
compute the critical t-threshold given alpha, the sample size, and the nature of the fit (quadratic). I was using excel so I used "T.inv".
t... | R: statistical test to identify samples with too high variability
I just looked at this.
My approach was:
compute the mean, standard deviation, and count for each set of samples
compute the critical t-threshold given alpha, the sample size, and the nature of the fi |
51,380 | R: statistical test to identify samples with too high variability | The "average variability" that you want to measure, should translate in Standard Deviation for statistics. It's pretty easy to compute STD in R, so look up the definition of Standard Deviation on google to see if it matches with what you want to find. | R: statistical test to identify samples with too high variability | The "average variability" that you want to measure, should translate in Standard Deviation for statistics. It's pretty easy to compute STD in R, so look up the definition of Standard Deviation on goog | R: statistical test to identify samples with too high variability
The "average variability" that you want to measure, should translate in Standard Deviation for statistics. It's pretty easy to compute STD in R, so look up the definition of Standard Deviation on google to see if it matches with what you want to find. | R: statistical test to identify samples with too high variability
The "average variability" that you want to measure, should translate in Standard Deviation for statistics. It's pretty easy to compute STD in R, so look up the definition of Standard Deviation on goog |
51,381 | PCA on non-centered data [duplicate] | PCA is sensitive to the scaling of the variables. ... One way of making the PCA less arbitrary is to use variables scaled so as to have unit variance, by standardizing the data and hence use the autocorrelation matrix instead of the autocovariance matrix as a basis for PCA. However, this compresses the fluctuations in ... | PCA on non-centered data [duplicate] | PCA is sensitive to the scaling of the variables. ... One way of making the PCA less arbitrary is to use variables scaled so as to have unit variance, by standardizing the data and hence use the autoc | PCA on non-centered data [duplicate]
PCA is sensitive to the scaling of the variables. ... One way of making the PCA less arbitrary is to use variables scaled so as to have unit variance, by standardizing the data and hence use the autocorrelation matrix instead of the autocovariance matrix as a basis for PCA. However,... | PCA on non-centered data [duplicate]
PCA is sensitive to the scaling of the variables. ... One way of making the PCA less arbitrary is to use variables scaled so as to have unit variance, by standardizing the data and hence use the autoc |
51,382 | How does cross validation work in R's gbm package? | If you want to estimate the error of the model and its corresponding variability when predicting new observations, after step 3. After fitting all the trees. Here the model that is being validated is the whole ensemble of weak learners. But naturally you could tune the hyperparameters using CV too. For example the opti... | How does cross validation work in R's gbm package? | If you want to estimate the error of the model and its corresponding variability when predicting new observations, after step 3. After fitting all the trees. Here the model that is being validated is | How does cross validation work in R's gbm package?
If you want to estimate the error of the model and its corresponding variability when predicting new observations, after step 3. After fitting all the trees. Here the model that is being validated is the whole ensemble of weak learners. But naturally you could tune the... | How does cross validation work in R's gbm package?
If you want to estimate the error of the model and its corresponding variability when predicting new observations, after step 3. After fitting all the trees. Here the model that is being validated is |
51,383 | How does cross validation work in R's gbm package? | Cross validation works by randomly (or by some other means) selecting rows into $K$ equally sized folds that are approximately balanced, training a classifier on $K-$ folds, testing on the remaining fold and then calculating a predictive loss function. This is repeated so that each fold is used as the test set. If you ... | How does cross validation work in R's gbm package? | Cross validation works by randomly (or by some other means) selecting rows into $K$ equally sized folds that are approximately balanced, training a classifier on $K-$ folds, testing on the remaining f | How does cross validation work in R's gbm package?
Cross validation works by randomly (or by some other means) selecting rows into $K$ equally sized folds that are approximately balanced, training a classifier on $K-$ folds, testing on the remaining fold and then calculating a predictive loss function. This is repeated... | How does cross validation work in R's gbm package?
Cross validation works by randomly (or by some other means) selecting rows into $K$ equally sized folds that are approximately balanced, training a classifier on $K-$ folds, testing on the remaining f |
51,384 | How do I compare date-ranges from a time series? [closed] | Estimate an ARIMA model for both time sections making sure that there are no Pulses/Level Shifts/Seasonal Pulses or Local Time trends unaccounted for. Estimate the model globally then use the CHOW Test to test the hypothesis that the parameters are the same for the two periods. | How do I compare date-ranges from a time series? [closed] | Estimate an ARIMA model for both time sections making sure that there are no Pulses/Level Shifts/Seasonal Pulses or Local Time trends unaccounted for. Estimate the model globally then use the CHOW Tes | How do I compare date-ranges from a time series? [closed]
Estimate an ARIMA model for both time sections making sure that there are no Pulses/Level Shifts/Seasonal Pulses or Local Time trends unaccounted for. Estimate the model globally then use the CHOW Test to test the hypothesis that the parameters are the same for ... | How do I compare date-ranges from a time series? [closed]
Estimate an ARIMA model for both time sections making sure that there are no Pulses/Level Shifts/Seasonal Pulses or Local Time trends unaccounted for. Estimate the model globally then use the CHOW Tes |
51,385 | Regression estimator where exponents are freely varying? | You will need to use non-linear optimization to solve this problem. Excel's solver should be able to find the parameters easily. Simply set the objective to minimize the sum of the squared residuals between the actual values you observed and the estimated values from your model. He is an example.
http://www.csupomona.e... | Regression estimator where exponents are freely varying? | You will need to use non-linear optimization to solve this problem. Excel's solver should be able to find the parameters easily. Simply set the objective to minimize the sum of the squared residuals b | Regression estimator where exponents are freely varying?
You will need to use non-linear optimization to solve this problem. Excel's solver should be able to find the parameters easily. Simply set the objective to minimize the sum of the squared residuals between the actual values you observed and the estimated values ... | Regression estimator where exponents are freely varying?
You will need to use non-linear optimization to solve this problem. Excel's solver should be able to find the parameters easily. Simply set the objective to minimize the sum of the squared residuals b |
51,386 | How to model time-varying correlation | 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.
It may be a little bit late for you, but for future re... | How to model time-varying correlation | 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 model time-varying correlation
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.
It may be a litt... | How to model time-varying correlation
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.
|
51,387 | What must someone know in statistics and machine learning? [closed] | The two worlds that you describe aren't really two different kinds of statistician, but rather:
"statistics on rails," to coin a phrase: an attempt to teach non-technical people enough to be able to use statistics in a few narrow contexts.
statistics proper, as understood by mathematicians, statisticians, data scient... | What must someone know in statistics and machine learning? [closed] | The two worlds that you describe aren't really two different kinds of statistician, but rather:
"statistics on rails," to coin a phrase: an attempt to teach non-technical people enough to be able to | What must someone know in statistics and machine learning? [closed]
The two worlds that you describe aren't really two different kinds of statistician, but rather:
"statistics on rails," to coin a phrase: an attempt to teach non-technical people enough to be able to use statistics in a few narrow contexts.
statistics... | What must someone know in statistics and machine learning? [closed]
The two worlds that you describe aren't really two different kinds of statistician, but rather:
"statistics on rails," to coin a phrase: an attempt to teach non-technical people enough to be able to |
51,388 | What must someone know in statistics and machine learning? [closed] | Speaking from a professional perspective (not an academic one), and based on having interviewed several candidates and having been interviewed myself many times as well, I would argue that deep or wide knowledge in stats is not considered as a "must know", but having a very solid grasp of the basics (linear regression,... | What must someone know in statistics and machine learning? [closed] | Speaking from a professional perspective (not an academic one), and based on having interviewed several candidates and having been interviewed myself many times as well, I would argue that deep or wid | What must someone know in statistics and machine learning? [closed]
Speaking from a professional perspective (not an academic one), and based on having interviewed several candidates and having been interviewed myself many times as well, I would argue that deep or wide knowledge in stats is not considered as a "must kn... | What must someone know in statistics and machine learning? [closed]
Speaking from a professional perspective (not an academic one), and based on having interviewed several candidates and having been interviewed myself many times as well, I would argue that deep or wid |
51,389 | What must someone know in statistics and machine learning? [closed] | What a person needs to know is going to depend on a lot of things. I can only answer from my perspective. I've worked as a data analyst for 20 years, working with researchers in the social, behavioral and medical sciences. I say "data analyst" to make clear that I view my job as a practical one: I help people figure ou... | What must someone know in statistics and machine learning? [closed] | What a person needs to know is going to depend on a lot of things. I can only answer from my perspective. I've worked as a data analyst for 20 years, working with researchers in the social, behavioral | What must someone know in statistics and machine learning? [closed]
What a person needs to know is going to depend on a lot of things. I can only answer from my perspective. I've worked as a data analyst for 20 years, working with researchers in the social, behavioral and medical sciences. I say "data analyst" to make ... | What must someone know in statistics and machine learning? [closed]
What a person needs to know is going to depend on a lot of things. I can only answer from my perspective. I've worked as a data analyst for 20 years, working with researchers in the social, behavioral |
51,390 | What must someone know in statistics and machine learning? [closed] | For a person doing work in statistics or doing work associated with statistics there is not really much clear must-know knowledge.
Obviously, people should be able to do simple and ordinary things, e.g. simple arithmetic.
But beyond that, statistics and machine learning is enormously broad and multidisciplinary. You m... | What must someone know in statistics and machine learning? [closed] | For a person doing work in statistics or doing work associated with statistics there is not really much clear must-know knowledge.
Obviously, people should be able to do simple and ordinary things, e | What must someone know in statistics and machine learning? [closed]
For a person doing work in statistics or doing work associated with statistics there is not really much clear must-know knowledge.
Obviously, people should be able to do simple and ordinary things, e.g. simple arithmetic.
But beyond that, statistics a... | What must someone know in statistics and machine learning? [closed]
For a person doing work in statistics or doing work associated with statistics there is not really much clear must-know knowledge.
Obviously, people should be able to do simple and ordinary things, e |
51,391 | What is the probability that a person will die on their birthday? | Sorry, a bit new here so please excuse me if this doesn't help too much.
The US Social Security Administration keeps records of births and deaths and has their information available for purchase (apparently for a hefty price): Here
However I found a source that claims to have bought it and is offering it for free (as w... | What is the probability that a person will die on their birthday? | Sorry, a bit new here so please excuse me if this doesn't help too much.
The US Social Security Administration keeps records of births and deaths and has their information available for purchase (appa | What is the probability that a person will die on their birthday?
Sorry, a bit new here so please excuse me if this doesn't help too much.
The US Social Security Administration keeps records of births and deaths and has their information available for purchase (apparently for a hefty price): Here
However I found a sour... | What is the probability that a person will die on their birthday?
Sorry, a bit new here so please excuse me if this doesn't help too much.
The US Social Security Administration keeps records of births and deaths and has their information available for purchase (appa |
51,392 | What is the probability that a person will die on their birthday? | We can be even more precise than @Mike Shi's data: the most dangerous of all birthdays is the very first one.
The 1st day mortality rates reported there are around 0.2 % for industrialized countries and 0.8 % average for all countries. Which means that the risk of dying on the day of birth is at least as high as the ri... | What is the probability that a person will die on their birthday? | We can be even more precise than @Mike Shi's data: the most dangerous of all birthdays is the very first one.
The 1st day mortality rates reported there are around 0.2 % for industrialized countries a | What is the probability that a person will die on their birthday?
We can be even more precise than @Mike Shi's data: the most dangerous of all birthdays is the very first one.
The 1st day mortality rates reported there are around 0.2 % for industrialized countries and 0.8 % average for all countries. Which means that t... | What is the probability that a person will die on their birthday?
We can be even more precise than @Mike Shi's data: the most dangerous of all birthdays is the very first one.
The 1st day mortality rates reported there are around 0.2 % for industrialized countries a |
51,393 | What is the probability that a person will die on their birthday? | Here's an argument why the probability of death on the birthday may be higher than on other days: Birthdays are emotionally charged days. More over, people tend to celebrate it somehow.. So there is an excess of factors (relative to the person's usual life style) that increase biological stress (excess emotions, excess... | What is the probability that a person will die on their birthday? | Here's an argument why the probability of death on the birthday may be higher than on other days: Birthdays are emotionally charged days. More over, people tend to celebrate it somehow.. So there is a | What is the probability that a person will die on their birthday?
Here's an argument why the probability of death on the birthday may be higher than on other days: Birthdays are emotionally charged days. More over, people tend to celebrate it somehow.. So there is an excess of factors (relative to the person's usual li... | What is the probability that a person will die on their birthday?
Here's an argument why the probability of death on the birthday may be higher than on other days: Birthdays are emotionally charged days. More over, people tend to celebrate it somehow.. So there is a |
51,394 | What is the probability that a person will die on their birthday? | The probability that a newborn dies within a year can be found in the life tables. For example, you can check out the periodic life tables and look at the column $q_x$ for $x=0$ in the human mortality database. This is not exactly want you want, but will give you an idea. | What is the probability that a person will die on their birthday? | The probability that a newborn dies within a year can be found in the life tables. For example, you can check out the periodic life tables and look at the column $q_x$ for $x=0$ in the human mortality | What is the probability that a person will die on their birthday?
The probability that a newborn dies within a year can be found in the life tables. For example, you can check out the periodic life tables and look at the column $q_x$ for $x=0$ in the human mortality database. This is not exactly want you want, but will... | What is the probability that a person will die on their birthday?
The probability that a newborn dies within a year can be found in the life tables. For example, you can check out the periodic life tables and look at the column $q_x$ for $x=0$ in the human mortality |
51,395 | What is the probability that a person will die on their birthday? | In addition to the other excellent answers, but there is a point none of them discussed: Birthdays are not uniformly distributed over the year, and neither are deathdays. That conspires such that the "statistical" probability is not 1/365. To get an idea of this effect, lets first assume they are both almost uniform, ... | What is the probability that a person will die on their birthday? | In addition to the other excellent answers, but there is a point none of them discussed: Birthdays are not uniformly distributed over the year, and neither are deathdays. That conspires such that the | What is the probability that a person will die on their birthday?
In addition to the other excellent answers, but there is a point none of them discussed: Birthdays are not uniformly distributed over the year, and neither are deathdays. That conspires such that the "statistical" probability is not 1/365. To get an ide... | What is the probability that a person will die on their birthday?
In addition to the other excellent answers, but there is a point none of them discussed: Birthdays are not uniformly distributed over the year, and neither are deathdays. That conspires such that the |
51,396 | What is the probability that a person will die on their birthday? | 1 out of 365 would be the correct odds, because you are guaranteed to die on one day out of a 365 day year... Therefore odds are 1 out of 365. | What is the probability that a person will die on their birthday? | 1 out of 365 would be the correct odds, because you are guaranteed to die on one day out of a 365 day year... Therefore odds are 1 out of 365. | What is the probability that a person will die on their birthday?
1 out of 365 would be the correct odds, because you are guaranteed to die on one day out of a 365 day year... Therefore odds are 1 out of 365. | What is the probability that a person will die on their birthday?
1 out of 365 would be the correct odds, because you are guaranteed to die on one day out of a 365 day year... Therefore odds are 1 out of 365. |
51,397 | Are all continuous random variables normally distributed? | No.
Lots of real life variables have distributions which are better described as other distributions. t-distributions (heavier tails) are common, as are various skewed distributions, for example, many real measurements must be positive, so greater than or equal to zero, but can have a long tail of high values. Quite a ... | Are all continuous random variables normally distributed? | No.
Lots of real life variables have distributions which are better described as other distributions. t-distributions (heavier tails) are common, as are various skewed distributions, for example, many | Are all continuous random variables normally distributed?
No.
Lots of real life variables have distributions which are better described as other distributions. t-distributions (heavier tails) are common, as are various skewed distributions, for example, many real measurements must be positive, so greater than or equal ... | Are all continuous random variables normally distributed?
No.
Lots of real life variables have distributions which are better described as other distributions. t-distributions (heavier tails) are common, as are various skewed distributions, for example, many |
51,398 | Are all continuous random variables normally distributed? | No.
There are many continuous probability distributions out of all the probability distributions. There are whole books containing nothing but such things.
Some of the non-normal continuous distributions introduced to new students of statistics include:
The continuous uniform distribution
Student's T distribution
The ... | Are all continuous random variables normally distributed? | No.
There are many continuous probability distributions out of all the probability distributions. There are whole books containing nothing but such things.
Some of the non-normal continuous distributi | Are all continuous random variables normally distributed?
No.
There are many continuous probability distributions out of all the probability distributions. There are whole books containing nothing but such things.
Some of the non-normal continuous distributions introduced to new students of statistics include:
The con... | Are all continuous random variables normally distributed?
No.
There are many continuous probability distributions out of all the probability distributions. There are whole books containing nothing but such things.
Some of the non-normal continuous distributi |
51,399 | Are all continuous random variables normally distributed? | Not necessarily. The shape of the distribution is contingent on the continuous random variable's PDF - which isn't expressly Gaussian. Some counterexamples include the student-t distribution and the Laplace distribution. | Are all continuous random variables normally distributed? | Not necessarily. The shape of the distribution is contingent on the continuous random variable's PDF - which isn't expressly Gaussian. Some counterexamples include the student-t distribution and the L | Are all continuous random variables normally distributed?
Not necessarily. The shape of the distribution is contingent on the continuous random variable's PDF - which isn't expressly Gaussian. Some counterexamples include the student-t distribution and the Laplace distribution. | Are all continuous random variables normally distributed?
Not necessarily. The shape of the distribution is contingent on the continuous random variable's PDF - which isn't expressly Gaussian. Some counterexamples include the student-t distribution and the L |
51,400 | Is R output reliable (specially IRT package ltm) [duplicate] | A good way to evaluate the quality of software is to perform simulations with known population parameters and observe how well these values can be recovered. Better yet, comparing parameter recovery to other known software is also a good idea since then you will have a general idea of what's happening if there are pecu... | Is R output reliable (specially IRT package ltm) [duplicate] | A good way to evaluate the quality of software is to perform simulations with known population parameters and observe how well these values can be recovered. Better yet, comparing parameter recovery t | Is R output reliable (specially IRT package ltm) [duplicate]
A good way to evaluate the quality of software is to perform simulations with known population parameters and observe how well these values can be recovered. Better yet, comparing parameter recovery to other known software is also a good idea since then you w... | Is R output reliable (specially IRT package ltm) [duplicate]
A good way to evaluate the quality of software is to perform simulations with known population parameters and observe how well these values can be recovered. Better yet, comparing parameter recovery t |
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