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 |
|---|---|---|---|---|---|---|
54,501 | Cluster standard error _versus_ fixed effects | You adjust for clustering at the level at which your experimental treatment is assigned. If your treatment is randomized by day of application, cluster by day. If your treatment is randomized by region, cluster by region. | Cluster standard error _versus_ fixed effects | You adjust for clustering at the level at which your experimental treatment is assigned. If your treatment is randomized by day of application, cluster by day. If your treatment is randomized by regio | Cluster standard error _versus_ fixed effects
You adjust for clustering at the level at which your experimental treatment is assigned. If your treatment is randomized by day of application, cluster by day. If your treatment is randomized by region, cluster by region. | Cluster standard error _versus_ fixed effects
You adjust for clustering at the level at which your experimental treatment is assigned. If your treatment is randomized by day of application, cluster by day. If your treatment is randomized by regio |
54,502 | Given a 95% confidence level, how do I demonstrate 95% of the intervals actually contain the population mean? | It helps to distil your problem down to something simple and clear. When using Excel, this means:
Strip out unnecessary and duplicate material.
Use meaningful names for ranges and variables rather than cell references wherever possible.
Make examples small.
Draw pictures of the data.
To illustrate, let me share a sp... | Given a 95% confidence level, how do I demonstrate 95% of the intervals actually contain the populat | It helps to distil your problem down to something simple and clear. When using Excel, this means:
Strip out unnecessary and duplicate material.
Use meaningful names for ranges and variables rather t | Given a 95% confidence level, how do I demonstrate 95% of the intervals actually contain the population mean?
It helps to distil your problem down to something simple and clear. When using Excel, this means:
Strip out unnecessary and duplicate material.
Use meaningful names for ranges and variables rather than cell r... | Given a 95% confidence level, how do I demonstrate 95% of the intervals actually contain the populat
It helps to distil your problem down to something simple and clear. When using Excel, this means:
Strip out unnecessary and duplicate material.
Use meaningful names for ranges and variables rather t |
54,503 | Given a 95% confidence level, how do I demonstrate 95% of the intervals actually contain the population mean? | In any case, you have to simulate an infinite number of samples to get the result you want.
Or rely on probability theory.
The probably that the confidence interval covers the mean is 0.95. If you do $n$ CI's, the number that cover the true mean will follow a binomial distribution $(n,p)$, with $p=0.95$.
So nothing is ... | Given a 95% confidence level, how do I demonstrate 95% of the intervals actually contain the populat | In any case, you have to simulate an infinite number of samples to get the result you want.
Or rely on probability theory.
The probably that the confidence interval covers the mean is 0.95. If you do | Given a 95% confidence level, how do I demonstrate 95% of the intervals actually contain the population mean?
In any case, you have to simulate an infinite number of samples to get the result you want.
Or rely on probability theory.
The probably that the confidence interval covers the mean is 0.95. If you do $n$ CI's, ... | Given a 95% confidence level, how do I demonstrate 95% of the intervals actually contain the populat
In any case, you have to simulate an infinite number of samples to get the result you want.
Or rely on probability theory.
The probably that the confidence interval covers the mean is 0.95. If you do |
54,504 | Correctness of regression with ARIMA errors model and coefficient interpretation issues | In any regression model, including a regression with ARMA errors, you must specify one less dummy variable than the number of categories. Intuitively, this is because if you know the value of 11 monthly dummy variables, then you know the value of the 12th. So it provides no new information.
There are two problems here.... | Correctness of regression with ARIMA errors model and coefficient interpretation issues | In any regression model, including a regression with ARMA errors, you must specify one less dummy variable than the number of categories. Intuitively, this is because if you know the value of 11 month | Correctness of regression with ARIMA errors model and coefficient interpretation issues
In any regression model, including a regression with ARMA errors, you must specify one less dummy variable than the number of categories. Intuitively, this is because if you know the value of 11 monthly dummy variables, then you kno... | Correctness of regression with ARIMA errors model and coefficient interpretation issues
In any regression model, including a regression with ARMA errors, you must specify one less dummy variable than the number of categories. Intuitively, this is because if you know the value of 11 month |
54,505 | What does it mean to correlate residuals in SEM? | It means that the unexplained variance from two variables are correlated. One way of thinking of this is as a partial correlation.
Say we have two regression equations:
\begin{equation}
Y_1i=\beta1_1 \ X_i+\epsilon1_i
\end{equation}
\begin{equation}
Y_2i=\beta2_1 \ X_i+\epsilon2_i
\end{equation}
Both equations have an... | What does it mean to correlate residuals in SEM? | It means that the unexplained variance from two variables are correlated. One way of thinking of this is as a partial correlation.
Say we have two regression equations:
\begin{equation}
Y_1i=\beta1_1 | What does it mean to correlate residuals in SEM?
It means that the unexplained variance from two variables are correlated. One way of thinking of this is as a partial correlation.
Say we have two regression equations:
\begin{equation}
Y_1i=\beta1_1 \ X_i+\epsilon1_i
\end{equation}
\begin{equation}
Y_2i=\beta2_1 \ X_i+... | What does it mean to correlate residuals in SEM?
It means that the unexplained variance from two variables are correlated. One way of thinking of this is as a partial correlation.
Say we have two regression equations:
\begin{equation}
Y_1i=\beta1_1 |
54,506 | How do you prepare longitudinal data for survival analysis? | Here is a quick example that shows how to arrange the data in a similar context.
Consider the following data.
> dataWide
id time status
1 1 0.88820072 1
2 2 0.05562832 0
3 3 5.24113929 1
4 4 2.91370906 1
For example, individual 1 had an event at $t = 0.888$, and individual 3 had an ev... | How do you prepare longitudinal data for survival analysis? | Here is a quick example that shows how to arrange the data in a similar context.
Consider the following data.
> dataWide
id time status
1 1 0.88820072 1
2 2 0.05562832 0
3 3 5.2 | How do you prepare longitudinal data for survival analysis?
Here is a quick example that shows how to arrange the data in a similar context.
Consider the following data.
> dataWide
id time status
1 1 0.88820072 1
2 2 0.05562832 0
3 3 5.24113929 1
4 4 2.91370906 1
For example, individu... | How do you prepare longitudinal data for survival analysis?
Here is a quick example that shows how to arrange the data in a similar context.
Consider the following data.
> dataWide
id time status
1 1 0.88820072 1
2 2 0.05562832 0
3 3 5.2 |
54,507 | How to interpret the model parameters of libsvm via MATLAB interface? | Support vector machine classifiers use the following decision function to determine the label for a test instance $\mathbf{z}$:
$f(\mathbf{z})=\mathtt{sign}\big(\sum_{i=1}^{totalSV} y_i \alpha_i \kappa(\mathbf{x}_i,\mathbf{z})-\rho\big)=\mathtt{sign}\big(\langle\mathbf{w},\Phi(\mathbf{z})\rangle-\rho\big)$,
where $\kap... | How to interpret the model parameters of libsvm via MATLAB interface? | Support vector machine classifiers use the following decision function to determine the label for a test instance $\mathbf{z}$:
$f(\mathbf{z})=\mathtt{sign}\big(\sum_{i=1}^{totalSV} y_i \alpha_i \kapp | How to interpret the model parameters of libsvm via MATLAB interface?
Support vector machine classifiers use the following decision function to determine the label for a test instance $\mathbf{z}$:
$f(\mathbf{z})=\mathtt{sign}\big(\sum_{i=1}^{totalSV} y_i \alpha_i \kappa(\mathbf{x}_i,\mathbf{z})-\rho\big)=\mathtt{sign}... | How to interpret the model parameters of libsvm via MATLAB interface?
Support vector machine classifiers use the following decision function to determine the label for a test instance $\mathbf{z}$:
$f(\mathbf{z})=\mathtt{sign}\big(\sum_{i=1}^{totalSV} y_i \alpha_i \kapp |
54,508 | How to interpret the model parameters of libsvm via MATLAB interface? | Nevermind, I found svm.cpp in the svmlight package and read the svm_predict function. It is written for the general case for n classes but for the simple case of two classes their logic boild down to
>> sv=model.SVs;
>> svc=model.sv_coef;
>> sv546=sv(1:546, :); %Since model.label is [1, -1] and model.nSV=[546; 246]
>> ... | How to interpret the model parameters of libsvm via MATLAB interface? | Nevermind, I found svm.cpp in the svmlight package and read the svm_predict function. It is written for the general case for n classes but for the simple case of two classes their logic boild down to
| How to interpret the model parameters of libsvm via MATLAB interface?
Nevermind, I found svm.cpp in the svmlight package and read the svm_predict function. It is written for the general case for n classes but for the simple case of two classes their logic boild down to
>> sv=model.SVs;
>> svc=model.sv_coef;
>> sv546=sv... | How to interpret the model parameters of libsvm via MATLAB interface?
Nevermind, I found svm.cpp in the svmlight package and read the svm_predict function. It is written for the general case for n classes but for the simple case of two classes their logic boild down to
|
54,509 | What is the proper name for a backward forecast? | Backcast, although I have seen hindcast as well. | What is the proper name for a backward forecast? | Backcast, although I have seen hindcast as well. | What is the proper name for a backward forecast?
Backcast, although I have seen hindcast as well. | What is the proper name for a backward forecast?
Backcast, although I have seen hindcast as well. |
54,510 | What is the proper name for a backward forecast? | I'm just learning Timeseries now but found this wonderful paper: Caporin and Sartore use the term back-calculation. They acknowledge it's not a common term, noting other used terms: retropolation, reconstruction, and back-casting (ARIMA).
see end of section 2:
Caporin, M., & Sartore, D. (2006). Methodological aspects ... | What is the proper name for a backward forecast? | I'm just learning Timeseries now but found this wonderful paper: Caporin and Sartore use the term back-calculation. They acknowledge it's not a common term, noting other used terms: retropolation, rec | What is the proper name for a backward forecast?
I'm just learning Timeseries now but found this wonderful paper: Caporin and Sartore use the term back-calculation. They acknowledge it's not a common term, noting other used terms: retropolation, reconstruction, and back-casting (ARIMA).
see end of section 2:
Caporin, ... | What is the proper name for a backward forecast?
I'm just learning Timeseries now but found this wonderful paper: Caporin and Sartore use the term back-calculation. They acknowledge it's not a common term, noting other used terms: retropolation, rec |
54,511 | NMDS and variance explained by vector fitting | I wouldn't place much stock in "rules of thumb" such as this. It is dependent upon so many things such as the number of variables, the number of sites, what dissimilarity you use etc. Also note that the vector fitting approach is inherently linear and we have no reason to presume that the relationship between the varia... | NMDS and variance explained by vector fitting | I wouldn't place much stock in "rules of thumb" such as this. It is dependent upon so many things such as the number of variables, the number of sites, what dissimilarity you use etc. Also note that t | NMDS and variance explained by vector fitting
I wouldn't place much stock in "rules of thumb" such as this. It is dependent upon so many things such as the number of variables, the number of sites, what dissimilarity you use etc. Also note that the vector fitting approach is inherently linear and we have no reason to p... | NMDS and variance explained by vector fitting
I wouldn't place much stock in "rules of thumb" such as this. It is dependent upon so many things such as the number of variables, the number of sites, what dissimilarity you use etc. Also note that t |
54,512 | Computing by hand the optimal threshold value for a biomarker using the Youden Index | It is a mistake to think that an optimum threshold can be computed without knowing the cost of a false positive and the cost of a false negative for a specific subject. And if those costs are not identical for all subjects, it is easy to see that no threshold should be used. ROC curves and Youden indexes are only use... | Computing by hand the optimal threshold value for a biomarker using the Youden Index | It is a mistake to think that an optimum threshold can be computed without knowing the cost of a false positive and the cost of a false negative for a specific subject. And if those costs are not ide | Computing by hand the optimal threshold value for a biomarker using the Youden Index
It is a mistake to think that an optimum threshold can be computed without knowing the cost of a false positive and the cost of a false negative for a specific subject. And if those costs are not identical for all subjects, it is easy... | Computing by hand the optimal threshold value for a biomarker using the Youden Index
It is a mistake to think that an optimum threshold can be computed without knowing the cost of a false positive and the cost of a false negative for a specific subject. And if those costs are not ide |
54,513 | What test should be used to tell if two linear regression lines are significantly different? | In this particular case, one of your lines has a known slope and intercept (intercept 0, slope 1), so you don't fit some larger interaction model, you can just jointly test whether the other model is consistent with the population intercept and slope being 0 and 1 respectively.
This is a standard thing for a linear mod... | What test should be used to tell if two linear regression lines are significantly different? | In this particular case, one of your lines has a known slope and intercept (intercept 0, slope 1), so you don't fit some larger interaction model, you can just jointly test whether the other model is | What test should be used to tell if two linear regression lines are significantly different?
In this particular case, one of your lines has a known slope and intercept (intercept 0, slope 1), so you don't fit some larger interaction model, you can just jointly test whether the other model is consistent with the populat... | What test should be used to tell if two linear regression lines are significantly different?
In this particular case, one of your lines has a known slope and intercept (intercept 0, slope 1), so you don't fit some larger interaction model, you can just jointly test whether the other model is |
54,514 | What test should be used to tell if two linear regression lines are significantly different? | Just estimate both lines in a single model using an interaction effect and test whether the interaction effect and the main effect equals 0. | What test should be used to tell if two linear regression lines are significantly different? | Just estimate both lines in a single model using an interaction effect and test whether the interaction effect and the main effect equals 0. | What test should be used to tell if two linear regression lines are significantly different?
Just estimate both lines in a single model using an interaction effect and test whether the interaction effect and the main effect equals 0. | What test should be used to tell if two linear regression lines are significantly different?
Just estimate both lines in a single model using an interaction effect and test whether the interaction effect and the main effect equals 0. |
54,515 | High censoring rate in survival analysis | The Kaplan-Meier estimator is not biased when a large proportion of individuals are censored. One of the problems we often observe is that the majority of power for the log-rank test is derived from early failure times which are difficult to observe in KM curves. It does mean that the median survival time is an unrelia... | High censoring rate in survival analysis | The Kaplan-Meier estimator is not biased when a large proportion of individuals are censored. One of the problems we often observe is that the majority of power for the log-rank test is derived from e | High censoring rate in survival analysis
The Kaplan-Meier estimator is not biased when a large proportion of individuals are censored. One of the problems we often observe is that the majority of power for the log-rank test is derived from early failure times which are difficult to observe in KM curves. It does mean th... | High censoring rate in survival analysis
The Kaplan-Meier estimator is not biased when a large proportion of individuals are censored. One of the problems we often observe is that the majority of power for the log-rank test is derived from e |
54,516 | High censoring rate in survival analysis | K-M does not work well for censoring proportions >50%. If you can analyze the distribution of your data, it is better to use a parametric method such as MLE. In alternative, you can also use imputation methods. | High censoring rate in survival analysis | K-M does not work well for censoring proportions >50%. If you can analyze the distribution of your data, it is better to use a parametric method such as MLE. In alternative, you can also use imputatio | High censoring rate in survival analysis
K-M does not work well for censoring proportions >50%. If you can analyze the distribution of your data, it is better to use a parametric method such as MLE. In alternative, you can also use imputation methods. | High censoring rate in survival analysis
K-M does not work well for censoring proportions >50%. If you can analyze the distribution of your data, it is better to use a parametric method such as MLE. In alternative, you can also use imputatio |
54,517 | vector fit interpretation NMDS | Vector fitting is a regression. Explicitly, the model fitted is
$$y = \beta_1 X_1 + \beta_2 X_2 + \varepsilon$$
where $y$ is the environmental variable requiring a vector, $X_i$ is the $i$th ordination "axis" score (here for the first two ordination "axes") and $\varepsilon$ the unexplained variance. Both $y$ and $X_i$... | vector fit interpretation NMDS | Vector fitting is a regression. Explicitly, the model fitted is
$$y = \beta_1 X_1 + \beta_2 X_2 + \varepsilon$$
where $y$ is the environmental variable requiring a vector, $X_i$ is the $i$th ordinatio | vector fit interpretation NMDS
Vector fitting is a regression. Explicitly, the model fitted is
$$y = \beta_1 X_1 + \beta_2 X_2 + \varepsilon$$
where $y$ is the environmental variable requiring a vector, $X_i$ is the $i$th ordination "axis" score (here for the first two ordination "axes") and $\varepsilon$ the unexplain... | vector fit interpretation NMDS
Vector fitting is a regression. Explicitly, the model fitted is
$$y = \beta_1 X_1 + \beta_2 X_2 + \varepsilon$$
where $y$ is the environmental variable requiring a vector, $X_i$ is the $i$th ordinatio |
54,518 | Ordering in VAR models | I will try to answer your second part of the question. If you understand this, I hope you will be able to answer the first part.
Ordering means placing the variables (all) in the decreasing order of exogenity . For example, if y1,y2, and y3 are three variables in the system and if we have from economic theory (or prev... | Ordering in VAR models | I will try to answer your second part of the question. If you understand this, I hope you will be able to answer the first part.
Ordering means placing the variables (all) in the decreasing order of | Ordering in VAR models
I will try to answer your second part of the question. If you understand this, I hope you will be able to answer the first part.
Ordering means placing the variables (all) in the decreasing order of exogenity . For example, if y1,y2, and y3 are three variables in the system and if we have from e... | Ordering in VAR models
I will try to answer your second part of the question. If you understand this, I hope you will be able to answer the first part.
Ordering means placing the variables (all) in the decreasing order of |
54,519 | Comparison between MDL and BIC | The Bayesian Infomration Criterion (BIC) is given as:
\begin{equation}\label{eq_BIC_FINAL}
BIC = \log f\left( {\bf{x}}|\hat{{\bf{\theta}}}_i ; H_i\right) - \frac{1}{2} \log \left| I\left(\hat{{\bf{\theta}}}_i \right)\right| + \frac{n_i}{2} \log 2 \pi e \overset{i}{\rightarrow} max,
\end{equation}
where $i=1,\cdots,M$ ... | Comparison between MDL and BIC | The Bayesian Infomration Criterion (BIC) is given as:
\begin{equation}\label{eq_BIC_FINAL}
BIC = \log f\left( {\bf{x}}|\hat{{\bf{\theta}}}_i ; H_i\right) - \frac{1}{2} \log \left| I\left(\hat{{\bf{\t | Comparison between MDL and BIC
The Bayesian Infomration Criterion (BIC) is given as:
\begin{equation}\label{eq_BIC_FINAL}
BIC = \log f\left( {\bf{x}}|\hat{{\bf{\theta}}}_i ; H_i\right) - \frac{1}{2} \log \left| I\left(\hat{{\bf{\theta}}}_i \right)\right| + \frac{n_i}{2} \log 2 \pi e \overset{i}{\rightarrow} max,
\end{... | Comparison between MDL and BIC
The Bayesian Infomration Criterion (BIC) is given as:
\begin{equation}\label{eq_BIC_FINAL}
BIC = \log f\left( {\bf{x}}|\hat{{\bf{\theta}}}_i ; H_i\right) - \frac{1}{2} \log \left| I\left(\hat{{\bf{\t |
54,520 | Comparison between MDL and BIC | No, if MDL is minimized by a model with two states while BIC is minimized by a model with four, that would not of itself imply that MDL is better.
But it's possible I missed something. What would make you think so? | Comparison between MDL and BIC | No, if MDL is minimized by a model with two states while BIC is minimized by a model with four, that would not of itself imply that MDL is better.
But it's possible I missed something. What would mak | Comparison between MDL and BIC
No, if MDL is minimized by a model with two states while BIC is minimized by a model with four, that would not of itself imply that MDL is better.
But it's possible I missed something. What would make you think so? | Comparison between MDL and BIC
No, if MDL is minimized by a model with two states while BIC is minimized by a model with four, that would not of itself imply that MDL is better.
But it's possible I missed something. What would mak |
54,521 | Comparison between MDL and BIC | In a mathematical sense, there is no such thing as "better." There is only larger or smaller according to some sort of norm or other function producing real numbers and/or intervals as output.
If you ever hear anyone say something is "optimal," I recommend asking, "In what sense?" This forces them to tell how they ca... | Comparison between MDL and BIC | In a mathematical sense, there is no such thing as "better." There is only larger or smaller according to some sort of norm or other function producing real numbers and/or intervals as output.
If you | Comparison between MDL and BIC
In a mathematical sense, there is no such thing as "better." There is only larger or smaller according to some sort of norm or other function producing real numbers and/or intervals as output.
If you ever hear anyone say something is "optimal," I recommend asking, "In what sense?" This ... | Comparison between MDL and BIC
In a mathematical sense, there is no such thing as "better." There is only larger or smaller according to some sort of norm or other function producing real numbers and/or intervals as output.
If you |
54,522 | Parametric vs. Nonparametric | Parametric does NOT mean "Bayesian based".
Here is one definition of "parametric statistics"
Parametric statistics is a branch of statistics that assumes data come
from a type of probability distribution and makes inferences about the parameters of the distribution
(From Wikipedia).
As Wikipedia goes on to note, mo... | Parametric vs. Nonparametric | Parametric does NOT mean "Bayesian based".
Here is one definition of "parametric statistics"
Parametric statistics is a branch of statistics that assumes data come
from a type of probability distri | Parametric vs. Nonparametric
Parametric does NOT mean "Bayesian based".
Here is one definition of "parametric statistics"
Parametric statistics is a branch of statistics that assumes data come
from a type of probability distribution and makes inferences about the parameters of the distribution
(From Wikipedia).
As ... | Parametric vs. Nonparametric
Parametric does NOT mean "Bayesian based".
Here is one definition of "parametric statistics"
Parametric statistics is a branch of statistics that assumes data come
from a type of probability distri |
54,523 | General definition of stochastic processes | Definitions
Recall that a random variable $X$ is a measurable function defined on a probability space $(\Omega,\mathcal{F},\mathbb{P})$ with values in a real vector space $V$. If you would like to focus on concepts and shed the mathematical details, you may think of it as a
consistent way to write numbers on tickets ... | General definition of stochastic processes | Definitions
Recall that a random variable $X$ is a measurable function defined on a probability space $(\Omega,\mathcal{F},\mathbb{P})$ with values in a real vector space $V$. If you would like to fo | General definition of stochastic processes
Definitions
Recall that a random variable $X$ is a measurable function defined on a probability space $(\Omega,\mathcal{F},\mathbb{P})$ with values in a real vector space $V$. If you would like to focus on concepts and shed the mathematical details, you may think of it as a
... | General definition of stochastic processes
Definitions
Recall that a random variable $X$ is a measurable function defined on a probability space $(\Omega,\mathcal{F},\mathbb{P})$ with values in a real vector space $V$. If you would like to fo |
54,524 | Practical interpretation for $u_t = \log(x_t) - \log(x_{t-1})$ | If a continuous-time process $x_t$ is geometric brownian motion it would have this property, or the discrete-time equivalent (geometric random walk).
A difference in logs is is (for $u_t$ small at least) effectively a percentage change.
See also the connection to the force of mortality (what actuaries used to call the... | Practical interpretation for $u_t = \log(x_t) - \log(x_{t-1})$ | If a continuous-time process $x_t$ is geometric brownian motion it would have this property, or the discrete-time equivalent (geometric random walk).
A difference in logs is is (for $u_t$ small at lea | Practical interpretation for $u_t = \log(x_t) - \log(x_{t-1})$
If a continuous-time process $x_t$ is geometric brownian motion it would have this property, or the discrete-time equivalent (geometric random walk).
A difference in logs is is (for $u_t$ small at least) effectively a percentage change.
See also the connec... | Practical interpretation for $u_t = \log(x_t) - \log(x_{t-1})$
If a continuous-time process $x_t$ is geometric brownian motion it would have this property, or the discrete-time equivalent (geometric random walk).
A difference in logs is is (for $u_t$ small at lea |
54,525 | Practical interpretation for $u_t = \log(x_t) - \log(x_{t-1})$ | If $u_{t}$ is near 0, then after multiplication by 100 it could be interpreted as percentage change of $x$ minus 100% from period $t-1$ to $t$ , that is beacause we could approximate $log(x_{t}/x_{t-1})$ by $x_{t}/x_{t-1}-1$ "very near" the point $x=1$, when $x$ is far away from 1 this approximation doesn't hold. Put f... | Practical interpretation for $u_t = \log(x_t) - \log(x_{t-1})$ | If $u_{t}$ is near 0, then after multiplication by 100 it could be interpreted as percentage change of $x$ minus 100% from period $t-1$ to $t$ , that is beacause we could approximate $log(x_{t}/x_{t-1 | Practical interpretation for $u_t = \log(x_t) - \log(x_{t-1})$
If $u_{t}$ is near 0, then after multiplication by 100 it could be interpreted as percentage change of $x$ minus 100% from period $t-1$ to $t$ , that is beacause we could approximate $log(x_{t}/x_{t-1})$ by $x_{t}/x_{t-1}-1$ "very near" the point $x=1$, whe... | Practical interpretation for $u_t = \log(x_t) - \log(x_{t-1})$
If $u_{t}$ is near 0, then after multiplication by 100 it could be interpreted as percentage change of $x$ minus 100% from period $t-1$ to $t$ , that is beacause we could approximate $log(x_{t}/x_{t-1 |
54,526 | Disagreement between normality tests and histogram graphs | It appears that your data can only take on positive values. In this case, the hypothesis of normality is often rejected. Normally distributed random variables range from positive to negative infinity, so only positive values would violate this. You could try taking the log of the observations and seeing whether these a... | Disagreement between normality tests and histogram graphs | It appears that your data can only take on positive values. In this case, the hypothesis of normality is often rejected. Normally distributed random variables range from positive to negative infinity, | Disagreement between normality tests and histogram graphs
It appears that your data can only take on positive values. In this case, the hypothesis of normality is often rejected. Normally distributed random variables range from positive to negative infinity, so only positive values would violate this. You could try tak... | Disagreement between normality tests and histogram graphs
It appears that your data can only take on positive values. In this case, the hypothesis of normality is often rejected. Normally distributed random variables range from positive to negative infinity, |
54,527 | How to apply Slutksy's Theorem? | Since user Max never turned his comment into an answer, to lay this one to officially rest:
By Lindeberg-Levy CLT indeed
$$\sqrt{n}( \bar X_n - \alpha) \xrightarrow{d} N(0,\sigma^2)$$
By the Law of Large Numbers
$$\bar Y_n \xrightarrow{p} \beta$$
Then we can apply Slutsky's theorem
$$Z_n=\frac{\sqrt{n}( \bar X_n - \al... | How to apply Slutksy's Theorem? | Since user Max never turned his comment into an answer, to lay this one to officially rest:
By Lindeberg-Levy CLT indeed
$$\sqrt{n}( \bar X_n - \alpha) \xrightarrow{d} N(0,\sigma^2)$$
By the Law of L | How to apply Slutksy's Theorem?
Since user Max never turned his comment into an answer, to lay this one to officially rest:
By Lindeberg-Levy CLT indeed
$$\sqrt{n}( \bar X_n - \alpha) \xrightarrow{d} N(0,\sigma^2)$$
By the Law of Large Numbers
$$\bar Y_n \xrightarrow{p} \beta$$
Then we can apply Slutsky's theorem
$$Z_... | How to apply Slutksy's Theorem?
Since user Max never turned his comment into an answer, to lay this one to officially rest:
By Lindeberg-Levy CLT indeed
$$\sqrt{n}( \bar X_n - \alpha) \xrightarrow{d} N(0,\sigma^2)$$
By the Law of L |
54,528 | Probability of an order statistic | Let's generalize a little: you have $n=8$ data in sorted order, $x_1 \lt x_2 \lt \cdots \lt x_n$, which you wish to divide randomly into groups of size $\alpha=4$ and $\beta=4$. Denote the division by the indicator of $\beta$: this is, in effect, an $n$-digit binary number having exactly $\beta$ ones. (Examples appear... | Probability of an order statistic | Let's generalize a little: you have $n=8$ data in sorted order, $x_1 \lt x_2 \lt \cdots \lt x_n$, which you wish to divide randomly into groups of size $\alpha=4$ and $\beta=4$. Denote the division b | Probability of an order statistic
Let's generalize a little: you have $n=8$ data in sorted order, $x_1 \lt x_2 \lt \cdots \lt x_n$, which you wish to divide randomly into groups of size $\alpha=4$ and $\beta=4$. Denote the division by the indicator of $\beta$: this is, in effect, an $n$-digit binary number having exac... | Probability of an order statistic
Let's generalize a little: you have $n=8$ data in sorted order, $x_1 \lt x_2 \lt \cdots \lt x_n$, which you wish to divide randomly into groups of size $\alpha=4$ and $\beta=4$. Denote the division b |
54,529 | What is the joint probability distribution of two same variables | $X$ is not jointly continuous with itself in the sense that there is no joint density
function (pdf) $f_{X,X}(s,t)$ that has positive value over a region of positive area
in the plane
with coordinate axes $s$ and $t$. All the probability mass lies on the straight line of slope $1$ through the origin (a region of zer... | What is the joint probability distribution of two same variables | $X$ is not jointly continuous with itself in the sense that there is no joint density
function (pdf) $f_{X,X}(s,t)$ that has positive value over a region of positive area
in the plane
with coordinat | What is the joint probability distribution of two same variables
$X$ is not jointly continuous with itself in the sense that there is no joint density
function (pdf) $f_{X,X}(s,t)$ that has positive value over a region of positive area
in the plane
with coordinate axes $s$ and $t$. All the probability mass lies on t... | What is the joint probability distribution of two same variables
$X$ is not jointly continuous with itself in the sense that there is no joint density
function (pdf) $f_{X,X}(s,t)$ that has positive value over a region of positive area
in the plane
with coordinat |
54,530 | What is the joint probability distribution of two same variables | $F_{(X,X)}(t,s) = P[X \le t,X \le s] = P[X \le \inf(s,t)]$
$f(s,t) = \frac{ \partial ^2F_{(X,X)}}{\partial s \partial t}(t,s) = \frac{\partial^2 (P[X \le t] \Large{1_{\{s=t\}}})}{\partial^2 t } $
Where
$\frac{\partial \Large{1_{\{s=t\}}}}{\partial t}$
$= lim_{\sigma \mapsto 0} \frac{1}{\sqrt{2\sigma \pi}} e^{\frac{... | What is the joint probability distribution of two same variables | $F_{(X,X)}(t,s) = P[X \le t,X \le s] = P[X \le \inf(s,t)]$
$f(s,t) = \frac{ \partial ^2F_{(X,X)}}{\partial s \partial t}(t,s) = \frac{\partial^2 (P[X \le t] \Large{1_{\{s=t\}}})}{\partial^2 t } $
Wh | What is the joint probability distribution of two same variables
$F_{(X,X)}(t,s) = P[X \le t,X \le s] = P[X \le \inf(s,t)]$
$f(s,t) = \frac{ \partial ^2F_{(X,X)}}{\partial s \partial t}(t,s) = \frac{\partial^2 (P[X \le t] \Large{1_{\{s=t\}}})}{\partial^2 t } $
Where
$\frac{\partial \Large{1_{\{s=t\}}}}{\partial t}$... | What is the joint probability distribution of two same variables
$F_{(X,X)}(t,s) = P[X \le t,X \le s] = P[X \le \inf(s,t)]$
$f(s,t) = \frac{ \partial ^2F_{(X,X)}}{\partial s \partial t}(t,s) = \frac{\partial^2 (P[X \le t] \Large{1_{\{s=t\}}})}{\partial^2 t } $
Wh |
54,531 | What is the joint probability distribution of two same variables | Random Discrete Variables Case:
For Y=X,
then pij = 0 as xi and Xj are always exclusive
for i=j and pij=pi for i=j. as xi and xi to happen in the same time has pi chance.
So E[X^2] = E[XX] = Sum (xi^2*pi) for both cases | What is the joint probability distribution of two same variables | Random Discrete Variables Case:
For Y=X,
then pij = 0 as xi and Xj are always exclusive
for i=j and pij=pi for i=j. as xi and xi to happen in the same time has pi chance.
So E[X^2] = E[XX] = Sum (xi | What is the joint probability distribution of two same variables
Random Discrete Variables Case:
For Y=X,
then pij = 0 as xi and Xj are always exclusive
for i=j and pij=pi for i=j. as xi and xi to happen in the same time has pi chance.
So E[X^2] = E[XX] = Sum (xi^2*pi) for both cases | What is the joint probability distribution of two same variables
Random Discrete Variables Case:
For Y=X,
then pij = 0 as xi and Xj are always exclusive
for i=j and pij=pi for i=j. as xi and xi to happen in the same time has pi chance.
So E[X^2] = E[XX] = Sum (xi |
54,532 | Dependent Bernoulli trials | There are expressions you can write down, but I hope you realize how uninformative they are. Saying that the variables are not known to be indpendent, without saying anything else, gives no usable information. It's like saying that you have a friend whose name is not known to be Bob, then asking what you can say about... | Dependent Bernoulli trials | There are expressions you can write down, but I hope you realize how uninformative they are. Saying that the variables are not known to be indpendent, without saying anything else, gives no usable inf | Dependent Bernoulli trials
There are expressions you can write down, but I hope you realize how uninformative they are. Saying that the variables are not known to be indpendent, without saying anything else, gives no usable information. It's like saying that you have a friend whose name is not known to be Bob, then as... | Dependent Bernoulli trials
There are expressions you can write down, but I hope you realize how uninformative they are. Saying that the variables are not known to be indpendent, without saying anything else, gives no usable inf |
54,533 | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$? | If you classify a fraction $k$ of your cases as positive then, because of the randomness, the same fraction $k$ of cases which should be positive will be classified positive (true positives), and the same fraction $k$ of cases which should be negative will be classified positive (false positives).
So the true positive... | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$? | If you classify a fraction $k$ of your cases as positive then, because of the randomness, the same fraction $k$ of cases which should be positive will be classified positive (true positives), and the | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$?
If you classify a fraction $k$ of your cases as positive then, because of the randomness, the same fraction $k$ of cases which should be positive will be classified positive (true positives), and the same fraction $k$ of cases which should be... | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$?
If you classify a fraction $k$ of your cases as positive then, because of the randomness, the same fraction $k$ of cases which should be positive will be classified positive (true positives), and the |
54,534 | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$? | Identity
Let $T$ be the event that a case is positive, and $R$ the event a case is predicted to be positive by a classifier.
Since $T$ and $T^c$ are mutually exclusive and collectively exhaustive, we can decompose $\mathbb{P}(R)$ as follows:
\begin{split}
\mathbb{P}(R) & = \mathbb{P}(R|T)\mathbb{P}(T)+\mathbb{P}(R|T^c)... | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$? | Identity
Let $T$ be the event that a case is positive, and $R$ the event a case is predicted to be positive by a classifier.
Since $T$ and $T^c$ are mutually exclusive and collectively exhaustive, we | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$?
Identity
Let $T$ be the event that a case is positive, and $R$ the event a case is predicted to be positive by a classifier.
Since $T$ and $T^c$ are mutually exclusive and collectively exhaustive, we can decompose $\mathbb{P}(R)$ as follows:
... | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$?
Identity
Let $T$ be the event that a case is positive, and $R$ the event a case is predicted to be positive by a classifier.
Since $T$ and $T^c$ are mutually exclusive and collectively exhaustive, we |
54,535 | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$? | A general classifier produces a point in the ROC space rather than a curve.
In order to consider a curve you typically further assume a parameterized classifier class of the form $f_t(X) = \mathbb{1}[h(X)>t]$, where $h(X)$ is a continuous random variable.
Now $(P(h(X)>t|Y=0),P(h(X)>t|Y=1))$ is a curve in the ROC space ... | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$? | A general classifier produces a point in the ROC space rather than a curve.
In order to consider a curve you typically further assume a parameterized classifier class of the form $f_t(X) = \mathbb{1}[ | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$?
A general classifier produces a point in the ROC space rather than a curve.
In order to consider a curve you typically further assume a parameterized classifier class of the form $f_t(X) = \mathbb{1}[h(X)>t]$, where $h(X)$ is a continuous ran... | Why is the ROC curve of a random classifier the line $\text{FPR}=\text{TPR}$?
A general classifier produces a point in the ROC space rather than a curve.
In order to consider a curve you typically further assume a parameterized classifier class of the form $f_t(X) = \mathbb{1}[ |
54,536 | How to improve neural network sensitivity with a lopsided binary outcome? | Yes, this is common with an imbalance in training data and some types of relationships.
Suppose bad students pass a tough course with probability $0$, while good students pass the course with probability $1/3$. If the only information you get to observe is whether the student is good or bad, then your most accurate pre... | How to improve neural network sensitivity with a lopsided binary outcome? | Yes, this is common with an imbalance in training data and some types of relationships.
Suppose bad students pass a tough course with probability $0$, while good students pass the course with probabil | How to improve neural network sensitivity with a lopsided binary outcome?
Yes, this is common with an imbalance in training data and some types of relationships.
Suppose bad students pass a tough course with probability $0$, while good students pass the course with probability $1/3$. If the only information you get to ... | How to improve neural network sensitivity with a lopsided binary outcome?
Yes, this is common with an imbalance in training data and some types of relationships.
Suppose bad students pass a tough course with probability $0$, while good students pass the course with probabil |
54,537 | Does a zig-zagging residual plot mean that normality has been violated? | +1 to @StatsStudent; your basic issue here is that you have few data. However, it might help to talk a little about what those plots are there for. Of course, you can get many things from looking at a plot, but those are the standard lm() diagnostic plots in R, so I will mention a conventional use for each.
Residua... | Does a zig-zagging residual plot mean that normality has been violated? | +1 to @StatsStudent; your basic issue here is that you have few data. However, it might help to talk a little about what those plots are there for. Of course, you can get many things from looking at | Does a zig-zagging residual plot mean that normality has been violated?
+1 to @StatsStudent; your basic issue here is that you have few data. However, it might help to talk a little about what those plots are there for. Of course, you can get many things from looking at a plot, but those are the standard lm() diagnos... | Does a zig-zagging residual plot mean that normality has been violated?
+1 to @StatsStudent; your basic issue here is that you have few data. However, it might help to talk a little about what those plots are there for. Of course, you can get many things from looking at |
54,538 | Does a zig-zagging residual plot mean that normality has been violated? | I don't see any cause for concern here -- No assumptions are obviously violated. But this is often difficult to confirm with so few data points. I think you are ok. | Does a zig-zagging residual plot mean that normality has been violated? | I don't see any cause for concern here -- No assumptions are obviously violated. But this is often difficult to confirm with so few data points. I think you are ok. | Does a zig-zagging residual plot mean that normality has been violated?
I don't see any cause for concern here -- No assumptions are obviously violated. But this is often difficult to confirm with so few data points. I think you are ok. | Does a zig-zagging residual plot mean that normality has been violated?
I don't see any cause for concern here -- No assumptions are obviously violated. But this is often difficult to confirm with so few data points. I think you are ok. |
54,539 | Interpreting multiple regression coefficients with 2 continuous variables interacting and 2 categorical variables interacting | As @gung said, it would help if you gave your full equation and DV, but, here, if the interaction between sex-female and mobility is -10.1, it means that the effect of high mobility on the dependent variable is 10.1 units less for women then men. Similarly, the effect of being female on the DV is 10.1 units less for hi... | Interpreting multiple regression coefficients with 2 continuous variables interacting and 2 categori | As @gung said, it would help if you gave your full equation and DV, but, here, if the interaction between sex-female and mobility is -10.1, it means that the effect of high mobility on the dependent v | Interpreting multiple regression coefficients with 2 continuous variables interacting and 2 categorical variables interacting
As @gung said, it would help if you gave your full equation and DV, but, here, if the interaction between sex-female and mobility is -10.1, it means that the effect of high mobility on the depen... | Interpreting multiple regression coefficients with 2 continuous variables interacting and 2 categori
As @gung said, it would help if you gave your full equation and DV, but, here, if the interaction between sex-female and mobility is -10.1, it means that the effect of high mobility on the dependent v |
54,540 | Modifying the time granularity of a state sequence | You can simply select the corresponding columns. In your case, this should be columns 1, 6, 11, ... You can get the column indices using the "seq" function:
column.5min <- seq(from = 1, to = 1440, by=5)
Now you can select the column, for instance using:
myseq5min <- myseq[, column.5min]
Here is an example using the "... | Modifying the time granularity of a state sequence | You can simply select the corresponding columns. In your case, this should be columns 1, 6, 11, ... You can get the column indices using the "seq" function:
column.5min <- seq(from = 1, to = 1440, by= | Modifying the time granularity of a state sequence
You can simply select the corresponding columns. In your case, this should be columns 1, 6, 11, ... You can get the column indices using the "seq" function:
column.5min <- seq(from = 1, to = 1440, by=5)
Now you can select the column, for instance using:
myseq5min <- m... | Modifying the time granularity of a state sequence
You can simply select the corresponding columns. In your case, this should be columns 1, 6, 11, ... You can get the column indices using the "seq" function:
column.5min <- seq(from = 1, to = 1440, by= |
54,541 | Modifying the time granularity of a state sequence | An alternative solution is to use the seqgranularity function provided by the TraMineRextras package.
This function changes the time granularity using different methods, currently either "first" state or "last" state, but other methods such as choosing the most frequent state in the aggregated spell should be implement... | Modifying the time granularity of a state sequence | An alternative solution is to use the seqgranularity function provided by the TraMineRextras package.
This function changes the time granularity using different methods, currently either "first" state | Modifying the time granularity of a state sequence
An alternative solution is to use the seqgranularity function provided by the TraMineRextras package.
This function changes the time granularity using different methods, currently either "first" state or "last" state, but other methods such as choosing the most frequen... | Modifying the time granularity of a state sequence
An alternative solution is to use the seqgranularity function provided by the TraMineRextras package.
This function changes the time granularity using different methods, currently either "first" state |
54,542 | What makes a GLM estimate the means differently from the actual sample means? | Let us have some data (shown below), predictand Y and two factors X1 and X2. X1 has 2 groups, and X2 has 3 groups. (In this particular example, the design is incomplete though, because combination X1=2 & X2=3 is absent.)
Let us run GLM command (shown). The settings are default: full factorial model, SS III type of squa... | What makes a GLM estimate the means differently from the actual sample means? | Let us have some data (shown below), predictand Y and two factors X1 and X2. X1 has 2 groups, and X2 has 3 groups. (In this particular example, the design is incomplete though, because combination X1= | What makes a GLM estimate the means differently from the actual sample means?
Let us have some data (shown below), predictand Y and two factors X1 and X2. X1 has 2 groups, and X2 has 3 groups. (In this particular example, the design is incomplete though, because combination X1=2 & X2=3 is absent.)
Let us run GLM comman... | What makes a GLM estimate the means differently from the actual sample means?
Let us have some data (shown below), predictand Y and two factors X1 and X2. X1 has 2 groups, and X2 has 3 groups. (In this particular example, the design is incomplete though, because combination X1= |
54,543 | What makes a GLM estimate the means differently from the actual sample means? | From my perspective this is completely legitimate question, which is in fact asked by many of my customers.
The mismatch can be attributed to the following:
Missing Data. SPSS by default excludes missing data case-wise. So if you happen to have missing observations in one factor, the cases on which the means are compu... | What makes a GLM estimate the means differently from the actual sample means? | From my perspective this is completely legitimate question, which is in fact asked by many of my customers.
The mismatch can be attributed to the following:
Missing Data. SPSS by default excludes mis | What makes a GLM estimate the means differently from the actual sample means?
From my perspective this is completely legitimate question, which is in fact asked by many of my customers.
The mismatch can be attributed to the following:
Missing Data. SPSS by default excludes missing data case-wise. So if you happen to h... | What makes a GLM estimate the means differently from the actual sample means?
From my perspective this is completely legitimate question, which is in fact asked by many of my customers.
The mismatch can be attributed to the following:
Missing Data. SPSS by default excludes mis |
54,544 | What makes a GLM estimate the means differently from the actual sample means? | If you fitted a model with only one factor with the same number of levels as means you want to estimate (well, maybe one less as you have the intercept term), then the estimated means should be exactly the observed means. When you add other covariates (variables in the model) then when you estimate a least squares mean... | What makes a GLM estimate the means differently from the actual sample means? | If you fitted a model with only one factor with the same number of levels as means you want to estimate (well, maybe one less as you have the intercept term), then the estimated means should be exactl | What makes a GLM estimate the means differently from the actual sample means?
If you fitted a model with only one factor with the same number of levels as means you want to estimate (well, maybe one less as you have the intercept term), then the estimated means should be exactly the observed means. When you add other c... | What makes a GLM estimate the means differently from the actual sample means?
If you fitted a model with only one factor with the same number of levels as means you want to estimate (well, maybe one less as you have the intercept term), then the estimated means should be exactl |
54,545 | How many random permutations to cover all possible permutations? | Permutation usually refers to something else, so it's probably better to call your problem "random binary words" or something similar.
The question of how long it takes to get at least one representative of each type is called the Coupon Collector Problem. If you assume that all binary words of length $N$ are equally ... | How many random permutations to cover all possible permutations? | Permutation usually refers to something else, so it's probably better to call your problem "random binary words" or something similar.
The question of how long it takes to get at least one representa | How many random permutations to cover all possible permutations?
Permutation usually refers to something else, so it's probably better to call your problem "random binary words" or something similar.
The question of how long it takes to get at least one representative of each type is called the Coupon Collector Proble... | How many random permutations to cover all possible permutations?
Permutation usually refers to something else, so it's probably better to call your problem "random binary words" or something similar.
The question of how long it takes to get at least one representa |
54,546 | Naive Bayes fails with a perfect predictor | Note that dat$X in your code is a numeric variable. The NaiveBayes implementation in klaR for numeric predictor variables calculates the mean and standard deviations of the predictor variable at each outcome level. Rather than dealing with standard deviations of 0, the klaR authors decided to throw an error.
If you cha... | Naive Bayes fails with a perfect predictor | Note that dat$X in your code is a numeric variable. The NaiveBayes implementation in klaR for numeric predictor variables calculates the mean and standard deviations of the predictor variable at each | Naive Bayes fails with a perfect predictor
Note that dat$X in your code is a numeric variable. The NaiveBayes implementation in klaR for numeric predictor variables calculates the mean and standard deviations of the predictor variable at each outcome level. Rather than dealing with standard deviations of 0, the klaR au... | Naive Bayes fails with a perfect predictor
Note that dat$X in your code is a numeric variable. The NaiveBayes implementation in klaR for numeric predictor variables calculates the mean and standard deviations of the predictor variable at each |
54,547 | Comparing P value from t test vs. Mann-Whitney test | It depends.
If you assume that the data are sampled from Gaussian distributions, then the t test has a bit more power (depending on sample size) so will -- on average -- have a lower P value. But only on average. For any particular set of data, the t test may give a higher or a lower P value.
If you don't assume the da... | Comparing P value from t test vs. Mann-Whitney test | It depends.
If you assume that the data are sampled from Gaussian distributions, then the t test has a bit more power (depending on sample size) so will -- on average -- have a lower P value. But only | Comparing P value from t test vs. Mann-Whitney test
It depends.
If you assume that the data are sampled from Gaussian distributions, then the t test has a bit more power (depending on sample size) so will -- on average -- have a lower P value. But only on average. For any particular set of data, the t test may give a h... | Comparing P value from t test vs. Mann-Whitney test
It depends.
If you assume that the data are sampled from Gaussian distributions, then the t test has a bit more power (depending on sample size) so will -- on average -- have a lower P value. But only |
54,548 | Comparing P value from t test vs. Mann-Whitney test | Here's an example showing exactly the behavior @Harvey describes above. We simulate varying degrees of normality by appending an outlier of varying degrees to a random normal draw of sample size 100 and calculate t-test p-values and U-test p-values for each, then plot as a function of how much of an outlier we appended... | Comparing P value from t test vs. Mann-Whitney test | Here's an example showing exactly the behavior @Harvey describes above. We simulate varying degrees of normality by appending an outlier of varying degrees to a random normal draw of sample size 100 a | Comparing P value from t test vs. Mann-Whitney test
Here's an example showing exactly the behavior @Harvey describes above. We simulate varying degrees of normality by appending an outlier of varying degrees to a random normal draw of sample size 100 and calculate t-test p-values and U-test p-values for each, then plot... | Comparing P value from t test vs. Mann-Whitney test
Here's an example showing exactly the behavior @Harvey describes above. We simulate varying degrees of normality by appending an outlier of varying degrees to a random normal draw of sample size 100 a |
54,549 | More interpretable measure of association than odds ratios for contingency tables with 0 counts | I am not exactly sure what you want to get finally, but have a look at this mosaic plots, testing independence:
And for the second dataset:
In both cases the data is dependent, but it is dependent in different manner here: if about the first plot we can just tell that 11 is too small (comparing to the whole table), t... | More interpretable measure of association than odds ratios for contingency tables with 0 counts | I am not exactly sure what you want to get finally, but have a look at this mosaic plots, testing independence:
And for the second dataset:
In both cases the data is dependent, but it is dependent i | More interpretable measure of association than odds ratios for contingency tables with 0 counts
I am not exactly sure what you want to get finally, but have a look at this mosaic plots, testing independence:
And for the second dataset:
In both cases the data is dependent, but it is dependent in different manner here:... | More interpretable measure of association than odds ratios for contingency tables with 0 counts
I am not exactly sure what you want to get finally, but have a look at this mosaic plots, testing independence:
And for the second dataset:
In both cases the data is dependent, but it is dependent i |
54,550 | More interpretable measure of association than odds ratios for contingency tables with 0 counts | In my mind, as an Epidemiologist, it depends on why there were zero counts.
If there is a particular combination of exposure and disease that is known to be possible but rare, then the usual way to proceed is to add some small number to each cell, usually 0.5 or so, and proceed from there, usually using Exact statistic... | More interpretable measure of association than odds ratios for contingency tables with 0 counts | In my mind, as an Epidemiologist, it depends on why there were zero counts.
If there is a particular combination of exposure and disease that is known to be possible but rare, then the usual way to pr | More interpretable measure of association than odds ratios for contingency tables with 0 counts
In my mind, as an Epidemiologist, it depends on why there were zero counts.
If there is a particular combination of exposure and disease that is known to be possible but rare, then the usual way to proceed is to add some sma... | More interpretable measure of association than odds ratios for contingency tables with 0 counts
In my mind, as an Epidemiologist, it depends on why there were zero counts.
If there is a particular combination of exposure and disease that is known to be possible but rare, then the usual way to pr |
54,551 | More interpretable measure of association than odds ratios for contingency tables with 0 counts | A convenient parameterisation of this problem is through the marginal and conditional probabilities. So we have a parameter for exposure $\pi_{E}$ and two parameters for disease given the exposure: $\pi_{D|E}$ and $\pi_{D|\overline{E}}$. Then we do a hypothesis test
$$H_0:\; \pi_{D|E}=\pi_{D|\overline{E}}$$
I would h... | More interpretable measure of association than odds ratios for contingency tables with 0 counts | A convenient parameterisation of this problem is through the marginal and conditional probabilities. So we have a parameter for exposure $\pi_{E}$ and two parameters for disease given the exposure: $ | More interpretable measure of association than odds ratios for contingency tables with 0 counts
A convenient parameterisation of this problem is through the marginal and conditional probabilities. So we have a parameter for exposure $\pi_{E}$ and two parameters for disease given the exposure: $\pi_{D|E}$ and $\pi_{D|\... | More interpretable measure of association than odds ratios for contingency tables with 0 counts
A convenient parameterisation of this problem is through the marginal and conditional probabilities. So we have a parameter for exposure $\pi_{E}$ and two parameters for disease given the exposure: $ |
54,552 | How do you calculate the expected value of mixed lognormal distribution? | I'll try to give an answer for the mixture case. Let's formalize the set-up. We consider a random variable $X$ and an indicator random variable $I$, with $P[I=1] = 1-P[I=2] = p$, independent of $X$. Furthermore, for the mixture we have that the law of $X$ given that $I=1$ is the law of $X_1$, which is Gaussian with mea... | How do you calculate the expected value of mixed lognormal distribution? | I'll try to give an answer for the mixture case. Let's formalize the set-up. We consider a random variable $X$ and an indicator random variable $I$, with $P[I=1] = 1-P[I=2] = p$, independent of $X$. F | How do you calculate the expected value of mixed lognormal distribution?
I'll try to give an answer for the mixture case. Let's formalize the set-up. We consider a random variable $X$ and an indicator random variable $I$, with $P[I=1] = 1-P[I=2] = p$, independent of $X$. Furthermore, for the mixture we have that the la... | How do you calculate the expected value of mixed lognormal distribution?
I'll try to give an answer for the mixture case. Let's formalize the set-up. We consider a random variable $X$ and an indicator random variable $I$, with $P[I=1] = 1-P[I=2] = p$, independent of $X$. F |
54,553 | How do you calculate the expected value of mixed lognormal distribution? | For instance, if you are dealing with a two component mixture, the first moment is calculated as follows:
$\mu_{\rm mixture}=\pi_{1} \cdot \mu_{1} + \pi_{2} \cdot \mu_{2}$,
where $\pi_{i}\ (i=1,2)$ stands for the components' weights and $\mu_{i}\ (i=1,2)$ for the means. | How do you calculate the expected value of mixed lognormal distribution? | For instance, if you are dealing with a two component mixture, the first moment is calculated as follows:
$\mu_{\rm mixture}=\pi_{1} \cdot \mu_{1} + \pi_{2} \cdot \mu_{2}$,
where $\pi_{i}\ (i=1,2)$ s | How do you calculate the expected value of mixed lognormal distribution?
For instance, if you are dealing with a two component mixture, the first moment is calculated as follows:
$\mu_{\rm mixture}=\pi_{1} \cdot \mu_{1} + \pi_{2} \cdot \mu_{2}$,
where $\pi_{i}\ (i=1,2)$ stands for the components' weights and $\mu_{i}\... | How do you calculate the expected value of mixed lognormal distribution?
For instance, if you are dealing with a two component mixture, the first moment is calculated as follows:
$\mu_{\rm mixture}=\pi_{1} \cdot \mu_{1} + \pi_{2} \cdot \mu_{2}$,
where $\pi_{i}\ (i=1,2)$ s |
54,554 | Using AIC, for model selection when both models are equally weighted, and one model has fewer parameters | There was a fairly good commentary in the Journal of Wildlife Management concerning uninformative parameters within the AIC framework.
Arnold, T. W. 2010. Uninformative parameters and model selection using Akaike’s Information Criterion. Journal of Wildlife Management 74:1175–1178. [Link].
We usually consider model... | Using AIC, for model selection when both models are equally weighted, and one model has fewer parame | There was a fairly good commentary in the Journal of Wildlife Management concerning uninformative parameters within the AIC framework.
Arnold, T. W. 2010. Uninformative parameters and model selectio | Using AIC, for model selection when both models are equally weighted, and one model has fewer parameters
There was a fairly good commentary in the Journal of Wildlife Management concerning uninformative parameters within the AIC framework.
Arnold, T. W. 2010. Uninformative parameters and model selection using Akaike’... | Using AIC, for model selection when both models are equally weighted, and one model has fewer parame
There was a fairly good commentary in the Journal of Wildlife Management concerning uninformative parameters within the AIC framework.
Arnold, T. W. 2010. Uninformative parameters and model selectio |
54,555 | Using AIC, for model selection when both models are equally weighted, and one model has fewer parameters | I couldn't find AICmodelSelect in any R package, searching in both R with ?? and Google. What package did you use? Or is it R?
In any case, if the log likelihoods are equal and the models have different numbers of parameters, then the AIC are not equal, which is what you have entered. The formula for AIC is
$AIC = 2k -... | Using AIC, for model selection when both models are equally weighted, and one model has fewer parame | I couldn't find AICmodelSelect in any R package, searching in both R with ?? and Google. What package did you use? Or is it R?
In any case, if the log likelihoods are equal and the models have differe | Using AIC, for model selection when both models are equally weighted, and one model has fewer parameters
I couldn't find AICmodelSelect in any R package, searching in both R with ?? and Google. What package did you use? Or is it R?
In any case, if the log likelihoods are equal and the models have different numbers of p... | Using AIC, for model selection when both models are equally weighted, and one model has fewer parame
I couldn't find AICmodelSelect in any R package, searching in both R with ?? and Google. What package did you use? Or is it R?
In any case, if the log likelihoods are equal and the models have differe |
54,556 | Using AIC, for model selection when both models are equally weighted, and one model has fewer parameters | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
Why do people strictly rely upon a criteria (ie AIC) to determine the "best" model? Why not use the principle... | Using AIC, for model selection when both models are equally weighted, and one model has fewer parame | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
| Using AIC, for model selection when both models are equally weighted, and one model has fewer parameters
Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
Why d... | Using AIC, for model selection when both models are equally weighted, and one model has fewer parame
Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
|
54,557 | Why don't the results of testing $H_0 : \beta = 0$ and $H_0 : {\rm cor}(X,Y)=0$ agree? | The reason is that you're testing two different hypotheses:
the Pearson correlation test is testing whether there is a non-zero correlation between the given predictor and the response variable, not taking into account the context supplied by the other predictors.
The $t$-test for the regression coefficient is testi... | Why don't the results of testing $H_0 : \beta = 0$ and $H_0 : {\rm cor}(X,Y)=0$ agree? | The reason is that you're testing two different hypotheses:
the Pearson correlation test is testing whether there is a non-zero correlation between the given predictor and the response variable, not | Why don't the results of testing $H_0 : \beta = 0$ and $H_0 : {\rm cor}(X,Y)=0$ agree?
The reason is that you're testing two different hypotheses:
the Pearson correlation test is testing whether there is a non-zero correlation between the given predictor and the response variable, not taking into account the context... | Why don't the results of testing $H_0 : \beta = 0$ and $H_0 : {\rm cor}(X,Y)=0$ agree?
The reason is that you're testing two different hypotheses:
the Pearson correlation test is testing whether there is a non-zero correlation between the given predictor and the response variable, not |
54,558 | Bias in classifier model selection | The key question is "have the test examples in the final cross-validation been involved in selecting any aspect of the model"; if the answer is "yes" then the performance estimate is likely to be biased. If the answer is "no" then it is probably unbiased.
For example nested cross-validation is fine (as all model choic... | Bias in classifier model selection | The key question is "have the test examples in the final cross-validation been involved in selecting any aspect of the model"; if the answer is "yes" then the performance estimate is likely to be bias | Bias in classifier model selection
The key question is "have the test examples in the final cross-validation been involved in selecting any aspect of the model"; if the answer is "yes" then the performance estimate is likely to be biased. If the answer is "no" then it is probably unbiased.
For example nested cross-val... | Bias in classifier model selection
The key question is "have the test examples in the final cross-validation been involved in selecting any aspect of the model"; if the answer is "yes" then the performance estimate is likely to be bias |
54,559 | Bias in classifier model selection | There is a difference between repeated cross-validation and nested-cross validation. The latter is useful for determining hyper-parameters and selecting features.
I've seen a couple of recent papers about the bias-variance implications of repeated cross-validation. Rodriguez and Lozano (IEEE T.PAMI 2010) test on art... | Bias in classifier model selection | There is a difference between repeated cross-validation and nested-cross validation. The latter is useful for determining hyper-parameters and selecting features.
I've seen a couple of recent paper | Bias in classifier model selection
There is a difference between repeated cross-validation and nested-cross validation. The latter is useful for determining hyper-parameters and selecting features.
I've seen a couple of recent papers about the bias-variance implications of repeated cross-validation. Rodriguez and Lo... | Bias in classifier model selection
There is a difference between repeated cross-validation and nested-cross validation. The latter is useful for determining hyper-parameters and selecting features.
I've seen a couple of recent paper |
54,560 | Does a regression tree strictly dominate OLS in prediction? | No Regression trees do not dominate OLS regression. OLS regression is intended for models where you want to estimate $E[Y|X]$ where $X$ is a set of predictors and the residuals from the model are continuous and Gaussian with mean $0$. Under that setting OLS should be superior to the regression tree. Remember that th... | Does a regression tree strictly dominate OLS in prediction? | No Regression trees do not dominate OLS regression. OLS regression is intended for models where you want to estimate $E[Y|X]$ where $X$ is a set of predictors and the residuals from the model are con | Does a regression tree strictly dominate OLS in prediction?
No Regression trees do not dominate OLS regression. OLS regression is intended for models where you want to estimate $E[Y|X]$ where $X$ is a set of predictors and the residuals from the model are continuous and Gaussian with mean $0$. Under that setting OLS ... | Does a regression tree strictly dominate OLS in prediction?
No Regression trees do not dominate OLS regression. OLS regression is intended for models where you want to estimate $E[Y|X]$ where $X$ is a set of predictors and the residuals from the model are con |
54,561 | How to do binary logistic regression on people (couples) clustered within homes? | For me, this sounds like a (more or less typical) dyadic data set and I would definitely control for dyadic dependencies (i.e. at the houshold level) via multilevel/structural equation modeling.
David Kenny owns a great website on Dyadic Analysis. He also is co-author of a book on Dyadic Data Analysis that is highly r... | How to do binary logistic regression on people (couples) clustered within homes? | For me, this sounds like a (more or less typical) dyadic data set and I would definitely control for dyadic dependencies (i.e. at the houshold level) via multilevel/structural equation modeling.
Davi | How to do binary logistic regression on people (couples) clustered within homes?
For me, this sounds like a (more or less typical) dyadic data set and I would definitely control for dyadic dependencies (i.e. at the houshold level) via multilevel/structural equation modeling.
David Kenny owns a great website on Dyadic ... | How to do binary logistic regression on people (couples) clustered within homes?
For me, this sounds like a (more or less typical) dyadic data set and I would definitely control for dyadic dependencies (i.e. at the houshold level) via multilevel/structural equation modeling.
Davi |
54,562 | How to do binary logistic regression on people (couples) clustered within homes? | One assumption of fixed-effects general linear models (e.g. "ordinary" logistic regression) is that observations are independent of each other. However, there is likely some dependency in the observations in your study. For example, two people living in the same household are more likely to have similar diets and simil... | How to do binary logistic regression on people (couples) clustered within homes? | One assumption of fixed-effects general linear models (e.g. "ordinary" logistic regression) is that observations are independent of each other. However, there is likely some dependency in the observat | How to do binary logistic regression on people (couples) clustered within homes?
One assumption of fixed-effects general linear models (e.g. "ordinary" logistic regression) is that observations are independent of each other. However, there is likely some dependency in the observations in your study. For example, two pe... | How to do binary logistic regression on people (couples) clustered within homes?
One assumption of fixed-effects general linear models (e.g. "ordinary" logistic regression) is that observations are independent of each other. However, there is likely some dependency in the observat |
54,563 | How to do binary logistic regression on people (couples) clustered within homes? | I see two possibilities. One would be to apply separate models (one for the female and one for the male member of the couple). The second possibility would be to have one model with an indicator variable to distinguish the male member from the female member of the couple. | How to do binary logistic regression on people (couples) clustered within homes? | I see two possibilities. One would be to apply separate models (one for the female and one for the male member of the couple). The second possibility would be to have one model with an indicator vari | How to do binary logistic regression on people (couples) clustered within homes?
I see two possibilities. One would be to apply separate models (one for the female and one for the male member of the couple). The second possibility would be to have one model with an indicator variable to distinguish the male member fro... | How to do binary logistic regression on people (couples) clustered within homes?
I see two possibilities. One would be to apply separate models (one for the female and one for the male member of the couple). The second possibility would be to have one model with an indicator vari |
54,564 | Does autocorrelation cause bias in the regression parameters in piecewise regression? | A regression parameter that is often forgotten is the variance of the residuals. This one will be biased if residuals are correlated. This means that p-values of whatever test you are performing have to be handled with great care.
Otherwise, if you fit a single line through something that is not linear (your case), you... | Does autocorrelation cause bias in the regression parameters in piecewise regression? | A regression parameter that is often forgotten is the variance of the residuals. This one will be biased if residuals are correlated. This means that p-values of whatever test you are performing have | Does autocorrelation cause bias in the regression parameters in piecewise regression?
A regression parameter that is often forgotten is the variance of the residuals. This one will be biased if residuals are correlated. This means that p-values of whatever test you are performing have to be handled with great care.
Oth... | Does autocorrelation cause bias in the regression parameters in piecewise regression?
A regression parameter that is often forgotten is the variance of the residuals. This one will be biased if residuals are correlated. This means that p-values of whatever test you are performing have |
54,565 | Does autocorrelation cause bias in the regression parameters in piecewise regression? | Thanks for sharing your data. It raises some interesting answers. To begin with a potentially useful model between y and x is which suggests a strong relationship between y and two previous y's and both a contemporaneous and lag 1 effect of X. The plot of actual/fit and forecast is and the cleansed ( outlier adjusted s... | Does autocorrelation cause bias in the regression parameters in piecewise regression? | Thanks for sharing your data. It raises some interesting answers. To begin with a potentially useful model between y and x is which suggests a strong relationship between y and two previous y's and bo | Does autocorrelation cause bias in the regression parameters in piecewise regression?
Thanks for sharing your data. It raises some interesting answers. To begin with a potentially useful model between y and x is which suggests a strong relationship between y and two previous y's and both a contemporaneous and lag 1 eff... | Does autocorrelation cause bias in the regression parameters in piecewise regression?
Thanks for sharing your data. It raises some interesting answers. To begin with a potentially useful model between y and x is which suggests a strong relationship between y and two previous y's and bo |
54,566 | Does autocorrelation cause bias in the regression parameters in piecewise regression? | I think piecewise regression means fitting several different lines at various cut points. It is not clear whether the number of cutoffs is prespecified and whether their locations are prespecified. Even if they are all prespecified it seems that each piece would be fit by ordinary regression and the problem of correl... | Does autocorrelation cause bias in the regression parameters in piecewise regression? | I think piecewise regression means fitting several different lines at various cut points. It is not clear whether the number of cutoffs is prespecified and whether their locations are prespecified. | Does autocorrelation cause bias in the regression parameters in piecewise regression?
I think piecewise regression means fitting several different lines at various cut points. It is not clear whether the number of cutoffs is prespecified and whether their locations are prespecified. Even if they are all prespecified ... | Does autocorrelation cause bias in the regression parameters in piecewise regression?
I think piecewise regression means fitting several different lines at various cut points. It is not clear whether the number of cutoffs is prespecified and whether their locations are prespecified. |
54,567 | Probability distribution of Fourier coefficients | The complex Fourier coefficients of a random series form a 2-D normal distribution in the complex plane (a Gaussian rotated around zero). When taking the magnitude of the complex spectrum, at each magnitude $r$ (the distance from the origin) the probability density will be $\int 2 \pi r dr$, multiplied by the Gaussian,... | Probability distribution of Fourier coefficients | The complex Fourier coefficients of a random series form a 2-D normal distribution in the complex plane (a Gaussian rotated around zero). When taking the magnitude of the complex spectrum, at each mag | Probability distribution of Fourier coefficients
The complex Fourier coefficients of a random series form a 2-D normal distribution in the complex plane (a Gaussian rotated around zero). When taking the magnitude of the complex spectrum, at each magnitude $r$ (the distance from the origin) the probability density will ... | Probability distribution of Fourier coefficients
The complex Fourier coefficients of a random series form a 2-D normal distribution in the complex plane (a Gaussian rotated around zero). When taking the magnitude of the complex spectrum, at each mag |
54,568 | Probability distribution of Fourier coefficients | You may find this topic dealt with in Brillinger, D.R. Time Series Analysis and Theory, in Chapter 4, particularly Theorem 4.4.2. I think in your case the answer is that the Fourier coefficients will have asymptotically a complex normal distribution, as pointed in the response by @micork. This will be the case rather g... | Probability distribution of Fourier coefficients | You may find this topic dealt with in Brillinger, D.R. Time Series Analysis and Theory, in Chapter 4, particularly Theorem 4.4.2. I think in your case the answer is that the Fourier coefficients will | Probability distribution of Fourier coefficients
You may find this topic dealt with in Brillinger, D.R. Time Series Analysis and Theory, in Chapter 4, particularly Theorem 4.4.2. I think in your case the answer is that the Fourier coefficients will have asymptotically a complex normal distribution, as pointed in the re... | Probability distribution of Fourier coefficients
You may find this topic dealt with in Brillinger, D.R. Time Series Analysis and Theory, in Chapter 4, particularly Theorem 4.4.2. I think in your case the answer is that the Fourier coefficients will |
54,569 | Significance of difference in means | They're probably not confidence intervals for the mean, but rather standard deviations from the data, just reported weirdly.
This interpretation is supported by the very confused presentation of results in the second quote. Fisher's exact test is a) not the same as a chi-squared test, b) almost certainly inappropria... | Significance of difference in means | They're probably not confidence intervals for the mean, but rather standard deviations from the data, just reported weirdly.
This interpretation is supported by the very confused presentation of res | Significance of difference in means
They're probably not confidence intervals for the mean, but rather standard deviations from the data, just reported weirdly.
This interpretation is supported by the very confused presentation of results in the second quote. Fisher's exact test is a) not the same as a chi-squared t... | Significance of difference in means
They're probably not confidence intervals for the mean, but rather standard deviations from the data, just reported weirdly.
This interpretation is supported by the very confused presentation of res |
54,570 | Significance of difference in means | The data aren't normal. I presume that the number of acute visits is an integer, i.e. you can't visit a patient 1.5 times. You either visit them once or twice.
As an example, here are some data:
Mean: 3.2 sd: 2.142
8 8 4 1 2 2 0 2 5 2 3 3 3 1 5 4 4 1 4 2
and
Mean:1.25 sd: 1.164
4 0 4 1 1 2 0 1 2 2 1 2 1 0 0 0 1 1 1 ... | Significance of difference in means | The data aren't normal. I presume that the number of acute visits is an integer, i.e. you can't visit a patient 1.5 times. You either visit them once or twice.
As an example, here are some data:
Mean | Significance of difference in means
The data aren't normal. I presume that the number of acute visits is an integer, i.e. you can't visit a patient 1.5 times. You either visit them once or twice.
As an example, here are some data:
Mean: 3.2 sd: 2.142
8 8 4 1 2 2 0 2 5 2 3 3 3 1 5 4 4 1 4 2
and
Mean:1.25 sd: 1.164
4 ... | Significance of difference in means
The data aren't normal. I presume that the number of acute visits is an integer, i.e. you can't visit a patient 1.5 times. You either visit them once or twice.
As an example, here are some data:
Mean |
54,571 | Inverse hyperbolic sine transformation: estimation of theta | The basic idea is as follows,
You have the IHS transformation
$$z_j = g_j(y_j;\theta)= \operatorname{sinh}^{-1}(\theta y_j)/\theta,\,\,j=1,...,n.$$
Then you have to find the value of $\theta$ that maximises the concentrated log-likelihood
$$L(\theta) = -\dfrac{n}{2}\log[g(\theta)^TMg(\theta)] - \dfrac{1}{2}\sum_j\log(1... | Inverse hyperbolic sine transformation: estimation of theta | The basic idea is as follows,
You have the IHS transformation
$$z_j = g_j(y_j;\theta)= \operatorname{sinh}^{-1}(\theta y_j)/\theta,\,\,j=1,...,n.$$
Then you have to find the value of $\theta$ that max | Inverse hyperbolic sine transformation: estimation of theta
The basic idea is as follows,
You have the IHS transformation
$$z_j = g_j(y_j;\theta)= \operatorname{sinh}^{-1}(\theta y_j)/\theta,\,\,j=1,...,n.$$
Then you have to find the value of $\theta$ that maximises the concentrated log-likelihood
$$L(\theta) = -\dfrac... | Inverse hyperbolic sine transformation: estimation of theta
The basic idea is as follows,
You have the IHS transformation
$$z_j = g_j(y_j;\theta)= \operatorname{sinh}^{-1}(\theta y_j)/\theta,\,\,j=1,...,n.$$
Then you have to find the value of $\theta$ that max |
54,572 | 2 period difference-in-differences fixed effects versus OLS | @Charlie is right. You only have two time periods, so there will inevitably be variation in the $i$-specific sample variances of $x_{it}$. In addition, even if you have programmed the simulation for there to be homogenous effects, due to small number of periods there will inevitably be some sample correlation between ... | 2 period difference-in-differences fixed effects versus OLS | @Charlie is right. You only have two time periods, so there will inevitably be variation in the $i$-specific sample variances of $x_{it}$. In addition, even if you have programmed the simulation for | 2 period difference-in-differences fixed effects versus OLS
@Charlie is right. You only have two time periods, so there will inevitably be variation in the $i$-specific sample variances of $x_{it}$. In addition, even if you have programmed the simulation for there to be homogenous effects, due to small number of perio... | 2 period difference-in-differences fixed effects versus OLS
@Charlie is right. You only have two time periods, so there will inevitably be variation in the $i$-specific sample variances of $x_{it}$. In addition, even if you have programmed the simulation for |
54,573 | 2 period difference-in-differences fixed effects versus OLS | First, I'm not sure what you mean by "fixed effects" regression as compared to OLS. In econometrics, at least, the standard fixed effects model is estimated via OLS. I'm assuming that you run a regression using group means instead of individual data, but I'm not sure.
In your model without $x$, it is fully flexible: al... | 2 period difference-in-differences fixed effects versus OLS | First, I'm not sure what you mean by "fixed effects" regression as compared to OLS. In econometrics, at least, the standard fixed effects model is estimated via OLS. I'm assuming that you run a regres | 2 period difference-in-differences fixed effects versus OLS
First, I'm not sure what you mean by "fixed effects" regression as compared to OLS. In econometrics, at least, the standard fixed effects model is estimated via OLS. I'm assuming that you run a regression using group means instead of individual data, but I'm n... | 2 period difference-in-differences fixed effects versus OLS
First, I'm not sure what you mean by "fixed effects" regression as compared to OLS. In econometrics, at least, the standard fixed effects model is estimated via OLS. I'm assuming that you run a regres |
54,574 | Is it appropriate to run correlations first and then a regression? | In my opinion, it is OK to inspect correlations first. In fact such exploratory data analysis is important, for one thing so that you know about any possible problems in advance with multicollinearity.
The best way to choose covariates in the first instance is by recourse to a priori understanding of the causal relatio... | Is it appropriate to run correlations first and then a regression? | In my opinion, it is OK to inspect correlations first. In fact such exploratory data analysis is important, for one thing so that you know about any possible problems in advance with multicollinearity | Is it appropriate to run correlations first and then a regression?
In my opinion, it is OK to inspect correlations first. In fact such exploratory data analysis is important, for one thing so that you know about any possible problems in advance with multicollinearity.
The best way to choose covariates in the first inst... | Is it appropriate to run correlations first and then a regression?
In my opinion, it is OK to inspect correlations first. In fact such exploratory data analysis is important, for one thing so that you know about any possible problems in advance with multicollinearity |
54,575 | How to use statistics to help buy a house? | I'm pretty sure this is the marriage problem. The idea is: You need to find a spouse. Researching information about a spouse is hard, and you can only look at one at a time. After some time spent looking (which we assume is constant), you can estimate a SpouseValue, which is how happy you would be married to this perso... | How to use statistics to help buy a house? | I'm pretty sure this is the marriage problem. The idea is: You need to find a spouse. Researching information about a spouse is hard, and you can only look at one at a time. After some time spent look | How to use statistics to help buy a house?
I'm pretty sure this is the marriage problem. The idea is: You need to find a spouse. Researching information about a spouse is hard, and you can only look at one at a time. After some time spent looking (which we assume is constant), you can estimate a SpouseValue, which is h... | How to use statistics to help buy a house?
I'm pretty sure this is the marriage problem. The idea is: You need to find a spouse. Researching information about a spouse is hard, and you can only look at one at a time. After some time spent look |
54,576 | Estimating the mutual information for two signal samples | I cannot immediately see a bug in your program. However, I see I few things that might alter the outcome of our results.
First of all, I would use $\sum_{ij} p_{ij} \left(\log p_{ij} - \log p_i - \log p_j\right)$ instead of $\sum_{ij} p_{ij} \log \frac{p_{ij}}{p_i\cdot p_j}$ for numerical stability. As a general rule,... | Estimating the mutual information for two signal samples | I cannot immediately see a bug in your program. However, I see I few things that might alter the outcome of our results.
First of all, I would use $\sum_{ij} p_{ij} \left(\log p_{ij} - \log p_i - \lo | Estimating the mutual information for two signal samples
I cannot immediately see a bug in your program. However, I see I few things that might alter the outcome of our results.
First of all, I would use $\sum_{ij} p_{ij} \left(\log p_{ij} - \log p_i - \log p_j\right)$ instead of $\sum_{ij} p_{ij} \log \frac{p_{ij}}{p... | Estimating the mutual information for two signal samples
I cannot immediately see a bug in your program. However, I see I few things that might alter the outcome of our results.
First of all, I would use $\sum_{ij} p_{ij} \left(\log p_{ij} - \log p_i - \lo |
54,577 | Logistic regression: Why we don't plot the residuals against the fitted values? | With this diagnostic plot, we're just looking at the residuals to see if anything leaps out at us - a clump of outliers, or, as happens with this data, a clear separation of the residuals into groups. It's merely one of several diagnostic plots you can, and should, do. We might suspect the two groups correspond to se... | Logistic regression: Why we don't plot the residuals against the fitted values? | With this diagnostic plot, we're just looking at the residuals to see if anything leaps out at us - a clump of outliers, or, as happens with this data, a clear separation of the residuals into groups. | Logistic regression: Why we don't plot the residuals against the fitted values?
With this diagnostic plot, we're just looking at the residuals to see if anything leaps out at us - a clump of outliers, or, as happens with this data, a clear separation of the residuals into groups. It's merely one of several diagnostic ... | Logistic regression: Why we don't plot the residuals against the fitted values?
With this diagnostic plot, we're just looking at the residuals to see if anything leaps out at us - a clump of outliers, or, as happens with this data, a clear separation of the residuals into groups. |
54,578 | How to generate random variates from random variables with known density? | If you know the pdf's for both, and the distribution from which you can sample, $f(x)$, encloses the distribution from which you want to sample, $g(x)$ (or can be made to do so by multiplying the likelihoods by some constant $c$), you can use an accept-reject algorithm. The gist of this approach is as follows:
Draw a... | How to generate random variates from random variables with known density? | If you know the pdf's for both, and the distribution from which you can sample, $f(x)$, encloses the distribution from which you want to sample, $g(x)$ (or can be made to do so by multiplying the like | How to generate random variates from random variables with known density?
If you know the pdf's for both, and the distribution from which you can sample, $f(x)$, encloses the distribution from which you want to sample, $g(x)$ (or can be made to do so by multiplying the likelihoods by some constant $c$), you can use an ... | How to generate random variates from random variables with known density?
If you know the pdf's for both, and the distribution from which you can sample, $f(x)$, encloses the distribution from which you want to sample, $g(x)$ (or can be made to do so by multiplying the like |
54,579 | Multiple regression and OLS. How to choose the best "non-linear" specification? | I've become rather enamoured of late with generalized additive modelling to handle non-linearity. The gam() function from the mgcv package for R makes things very easy as it incorporates automated generalized cross-validation to avoid overfitting. | Multiple regression and OLS. How to choose the best "non-linear" specification? | I've become rather enamoured of late with generalized additive modelling to handle non-linearity. The gam() function from the mgcv package for R makes things very easy as it incorporates automated gen | Multiple regression and OLS. How to choose the best "non-linear" specification?
I've become rather enamoured of late with generalized additive modelling to handle non-linearity. The gam() function from the mgcv package for R makes things very easy as it incorporates automated generalized cross-validation to avoid overf... | Multiple regression and OLS. How to choose the best "non-linear" specification?
I've become rather enamoured of late with generalized additive modelling to handle non-linearity. The gam() function from the mgcv package for R makes things very easy as it incorporates automated gen |
54,580 | Multiple regression and OLS. How to choose the best "non-linear" specification? | I've never heard of gretl but a parametric version of the excellent gam suggestion by Mike is to use additive regressions such as restricted cubic splines (natural splines). R and Stata make this easy to do. With regression splines (piecewise polynomials) you can model almost any relationship that is fairly smooth, a... | Multiple regression and OLS. How to choose the best "non-linear" specification? | I've never heard of gretl but a parametric version of the excellent gam suggestion by Mike is to use additive regressions such as restricted cubic splines (natural splines). R and Stata make this eas | Multiple regression and OLS. How to choose the best "non-linear" specification?
I've never heard of gretl but a parametric version of the excellent gam suggestion by Mike is to use additive regressions such as restricted cubic splines (natural splines). R and Stata make this easy to do. With regression splines (piece... | Multiple regression and OLS. How to choose the best "non-linear" specification?
I've never heard of gretl but a parametric version of the excellent gam suggestion by Mike is to use additive regressions such as restricted cubic splines (natural splines). R and Stata make this eas |
54,581 | How to perform model selection in GEE in R | If you want to select amongst pre-specified models, this should work the same with GEE as elsewhere. For example, if you were comparing a nested model to a full model, you could test that. If the models weren't nested, you could use an informational criterion (such as the QIC) to help adjudicate between them. Anothe... | How to perform model selection in GEE in R | If you want to select amongst pre-specified models, this should work the same with GEE as elsewhere. For example, if you were comparing a nested model to a full model, you could test that. If the mo | How to perform model selection in GEE in R
If you want to select amongst pre-specified models, this should work the same with GEE as elsewhere. For example, if you were comparing a nested model to a full model, you could test that. If the models weren't nested, you could use an informational criterion (such as the QI... | How to perform model selection in GEE in R
If you want to select amongst pre-specified models, this should work the same with GEE as elsewhere. For example, if you were comparing a nested model to a full model, you could test that. If the mo |
54,582 | How to perform model selection in GEE in R | [UPDATE: I improved on the code below and made a small R package hosted on GitHub: https://github.com/djhocking/qicpack]
I figured out a solution for calculating QIC from geepack package output. My code is below. This is one of the first functions I've ever written, so I apologize if it's messy but hopefully others fi... | How to perform model selection in GEE in R | [UPDATE: I improved on the code below and made a small R package hosted on GitHub: https://github.com/djhocking/qicpack]
I figured out a solution for calculating QIC from geepack package output. My c | How to perform model selection in GEE in R
[UPDATE: I improved on the code below and made a small R package hosted on GitHub: https://github.com/djhocking/qicpack]
I figured out a solution for calculating QIC from geepack package output. My code is below. This is one of the first functions I've ever written, so I apol... | How to perform model selection in GEE in R
[UPDATE: I improved on the code below and made a small R package hosted on GitHub: https://github.com/djhocking/qicpack]
I figured out a solution for calculating QIC from geepack package output. My c |
54,583 | How to perform model selection in GEE in R | You can use the model.sel command from the MuMIn package:
library(MuMIn)
model.sel(gee.0, gee.1, gee.2, gee.3, rank = QIC)
This uses MSE of prediction for model selection (Mean square error of prediction)--The smaller the better! | How to perform model selection in GEE in R | You can use the model.sel command from the MuMIn package:
library(MuMIn)
model.sel(gee.0, gee.1, gee.2, gee.3, rank = QIC)
This uses MSE of prediction for model selection (Mean square error of predic | How to perform model selection in GEE in R
You can use the model.sel command from the MuMIn package:
library(MuMIn)
model.sel(gee.0, gee.1, gee.2, gee.3, rank = QIC)
This uses MSE of prediction for model selection (Mean square error of prediction)--The smaller the better! | How to perform model selection in GEE in R
You can use the model.sel command from the MuMIn package:
library(MuMIn)
model.sel(gee.0, gee.1, gee.2, gee.3, rank = QIC)
This uses MSE of prediction for model selection (Mean square error of predic |
54,584 | Convergence results for block-gibbs sampling? | Much faster than what? Univariate Gibbs sampling?
The two stage Gibbs sampling is certainly the most studied type of Gibbs sampling starting with Tanner and Wong (1987, JASA). There is in particular a very achieved paper by Liu, Wong and Kong (1994, Biometrika), which shows that the correlation between the $X_t$'s (and... | Convergence results for block-gibbs sampling? | Much faster than what? Univariate Gibbs sampling?
The two stage Gibbs sampling is certainly the most studied type of Gibbs sampling starting with Tanner and Wong (1987, JASA). There is in particular a | Convergence results for block-gibbs sampling?
Much faster than what? Univariate Gibbs sampling?
The two stage Gibbs sampling is certainly the most studied type of Gibbs sampling starting with Tanner and Wong (1987, JASA). There is in particular a very achieved paper by Liu, Wong and Kong (1994, Biometrika), which shows... | Convergence results for block-gibbs sampling?
Much faster than what? Univariate Gibbs sampling?
The two stage Gibbs sampling is certainly the most studied type of Gibbs sampling starting with Tanner and Wong (1987, JASA). There is in particular a |
54,585 | Jaccard Indexes and PCA | the Jaccard index is a positive definite kernel as can be checked in A Short Tour of Kernel Methods for Graphs, by Gärtner, Le, and Smola; see definition 1.4 and references below.
Doing a PCA on a matrix of Jaccard similarities is akin to doing kernel PCA,
that is doing PCA in the reproducing kernel Hilbert space of fu... | Jaccard Indexes and PCA | the Jaccard index is a positive definite kernel as can be checked in A Short Tour of Kernel Methods for Graphs, by Gärtner, Le, and Smola; see definition 1.4 and references below.
Doing a PCA on a mat | Jaccard Indexes and PCA
the Jaccard index is a positive definite kernel as can be checked in A Short Tour of Kernel Methods for Graphs, by Gärtner, Le, and Smola; see definition 1.4 and references below.
Doing a PCA on a matrix of Jaccard similarities is akin to doing kernel PCA,
that is doing PCA in the reproducing ke... | Jaccard Indexes and PCA
the Jaccard index is a positive definite kernel as can be checked in A Short Tour of Kernel Methods for Graphs, by Gärtner, Le, and Smola; see definition 1.4 and references below.
Doing a PCA on a mat |
54,586 | Jaccard Indexes and PCA | Linear Principal Component or Factor analyses are based on linear regression model and this implies that the input similarities must be covariances, correlations, cosines, or sum-of-cross-products (all these similarities are known as scalar products). You may input any other sort of similarity, such as Jaccard measure ... | Jaccard Indexes and PCA | Linear Principal Component or Factor analyses are based on linear regression model and this implies that the input similarities must be covariances, correlations, cosines, or sum-of-cross-products (al | Jaccard Indexes and PCA
Linear Principal Component or Factor analyses are based on linear regression model and this implies that the input similarities must be covariances, correlations, cosines, or sum-of-cross-products (all these similarities are known as scalar products). You may input any other sort of similarity, ... | Jaccard Indexes and PCA
Linear Principal Component or Factor analyses are based on linear regression model and this implies that the input similarities must be covariances, correlations, cosines, or sum-of-cross-products (al |
54,587 | Jaccard Indexes and PCA | In PCA, we attempt to "concisely" explain the variation in POSSIBLY CORRELATED data using principal components which are pairwise orthogonal to each other. The variation in data is represented by the variance-covariance matrix.
On the other hand, a jaccard index is a similarity coefficient. Similarity and correlation ... | Jaccard Indexes and PCA | In PCA, we attempt to "concisely" explain the variation in POSSIBLY CORRELATED data using principal components which are pairwise orthogonal to each other. The variation in data is represented by the | Jaccard Indexes and PCA
In PCA, we attempt to "concisely" explain the variation in POSSIBLY CORRELATED data using principal components which are pairwise orthogonal to each other. The variation in data is represented by the variance-covariance matrix.
On the other hand, a jaccard index is a similarity coefficient. Sim... | Jaccard Indexes and PCA
In PCA, we attempt to "concisely" explain the variation in POSSIBLY CORRELATED data using principal components which are pairwise orthogonal to each other. The variation in data is represented by the |
54,588 | How to transform data to normality? | For financial data I have successfully used heavy-tail Lambert W x Gaussian transformations.
Pyhon: gaussianize is an sklearn-type implementation of the IGMM algorithm in Python.
C++: the lamW R package has an elegant (and fast) C++ implementation of Lambert's W function. This can be a starting point for a full C++ i... | How to transform data to normality? | For financial data I have successfully used heavy-tail Lambert W x Gaussian transformations.
Pyhon: gaussianize is an sklearn-type implementation of the IGMM algorithm in Python.
C++: the lamW R pack | How to transform data to normality?
For financial data I have successfully used heavy-tail Lambert W x Gaussian transformations.
Pyhon: gaussianize is an sklearn-type implementation of the IGMM algorithm in Python.
C++: the lamW R package has an elegant (and fast) C++ implementation of Lambert's W function. This can ... | How to transform data to normality?
For financial data I have successfully used heavy-tail Lambert W x Gaussian transformations.
Pyhon: gaussianize is an sklearn-type implementation of the IGMM algorithm in Python.
C++: the lamW R pack |
54,589 | How to transform data to normality? | It appears that you are just asking for a test for normality. If so, Shapiro-Wilk is hard to beat. This is not, however, the easiest test in the pantheon to implement.
Why not just use R? The shapiro.test function will do the work for you. | How to transform data to normality? | It appears that you are just asking for a test for normality. If so, Shapiro-Wilk is hard to beat. This is not, however, the easiest test in the pantheon to implement.
Why not just use R? The sha | How to transform data to normality?
It appears that you are just asking for a test for normality. If so, Shapiro-Wilk is hard to beat. This is not, however, the easiest test in the pantheon to implement.
Why not just use R? The shapiro.test function will do the work for you. | How to transform data to normality?
It appears that you are just asking for a test for normality. If so, Shapiro-Wilk is hard to beat. This is not, however, the easiest test in the pantheon to implement.
Why not just use R? The sha |
54,590 | Simpson's paradox or confounding? | Simpson's paradox is an extreme form of confounding where the apparent sign of correlation is reversed; you haven't said this is the position here.
I can see at least three possibilities here: the heterogenity between the subgroups, the reduction in sample sizes in each, and poor definition of the subgroups which presu... | Simpson's paradox or confounding? | Simpson's paradox is an extreme form of confounding where the apparent sign of correlation is reversed; you haven't said this is the position here.
I can see at least three possibilities here: the het | Simpson's paradox or confounding?
Simpson's paradox is an extreme form of confounding where the apparent sign of correlation is reversed; you haven't said this is the position here.
I can see at least three possibilities here: the heterogenity between the subgroups, the reduction in sample sizes in each, and poor defin... | Simpson's paradox or confounding?
Simpson's paradox is an extreme form of confounding where the apparent sign of correlation is reversed; you haven't said this is the position here.
I can see at least three possibilities here: the het |
54,591 | Transform log posteriors to the original posteriors | Consider the expression:
$$\frac{exp(A)}{exp(A)+exp(B)}$$
The generic strategy to compute the above expression when $exp(A)$ overflows would be to transform as follows:
$$\frac{1}{1+exp(B-A)}$$
For example R chokes on:
$$\frac{exp(1100)}{exp(1100)+exp(1104)}$$
But, happily computes the following transformation to yield... | Transform log posteriors to the original posteriors | Consider the expression:
$$\frac{exp(A)}{exp(A)+exp(B)}$$
The generic strategy to compute the above expression when $exp(A)$ overflows would be to transform as follows:
$$\frac{1}{1+exp(B-A)}$$
For ex | Transform log posteriors to the original posteriors
Consider the expression:
$$\frac{exp(A)}{exp(A)+exp(B)}$$
The generic strategy to compute the above expression when $exp(A)$ overflows would be to transform as follows:
$$\frac{1}{1+exp(B-A)}$$
For example R chokes on:
$$\frac{exp(1100)}{exp(1100)+exp(1104)}$$
But, ha... | Transform log posteriors to the original posteriors
Consider the expression:
$$\frac{exp(A)}{exp(A)+exp(B)}$$
The generic strategy to compute the above expression when $exp(A)$ overflows would be to transform as follows:
$$\frac{1}{1+exp(B-A)}$$
For ex |
54,592 | Transform log posteriors to the original posteriors | In the general case, use
$$
\dfrac{ \exp\{A_i-\max_j(A_j)\} }{ \sum_k \exp\{A_k-\max_j(A_j)\} }
$$
to avoid overflows. I always use this approach when computing Bayes factors and probabilities. | Transform log posteriors to the original posteriors | In the general case, use
$$
\dfrac{ \exp\{A_i-\max_j(A_j)\} }{ \sum_k \exp\{A_k-\max_j(A_j)\} }
$$
to avoid overflows. I always use this approach when computing Bayes factors and probabilities. | Transform log posteriors to the original posteriors
In the general case, use
$$
\dfrac{ \exp\{A_i-\max_j(A_j)\} }{ \sum_k \exp\{A_k-\max_j(A_j)\} }
$$
to avoid overflows. I always use this approach when computing Bayes factors and probabilities. | Transform log posteriors to the original posteriors
In the general case, use
$$
\dfrac{ \exp\{A_i-\max_j(A_j)\} }{ \sum_k \exp\{A_k-\max_j(A_j)\} }
$$
to avoid overflows. I always use this approach when computing Bayes factors and probabilities. |
54,593 | How to plot multiple users' deviations from predictions of bandwidth consumption over time? | You might want to plot this as the cumulated deviation from the predicted values. Whether this makes sense depends on what the billing/analysis period is: If the group has to stay below a certain limit for each quarter, this would allow them to see whether they're on track for reaching that goal. If their account balan... | How to plot multiple users' deviations from predictions of bandwidth consumption over time? | You might want to plot this as the cumulated deviation from the predicted values. Whether this makes sense depends on what the billing/analysis period is: If the group has to stay below a certain limi | How to plot multiple users' deviations from predictions of bandwidth consumption over time?
You might want to plot this as the cumulated deviation from the predicted values. Whether this makes sense depends on what the billing/analysis period is: If the group has to stay below a certain limit for each quarter, this wou... | How to plot multiple users' deviations from predictions of bandwidth consumption over time?
You might want to plot this as the cumulated deviation from the predicted values. Whether this makes sense depends on what the billing/analysis period is: If the group has to stay below a certain limi |
54,594 | How to plot multiple users' deviations from predictions of bandwidth consumption over time? | For these particular data, I would make a line plot, like the following.
Here's the R code I used:
dat <- data.frame(user1 = c(-0.075, -0.09, 0.32, -0.242, -0.368, -0.401, -0.73, -0.367, -0.294, -0.043, 1.296, 0.075, -0.373),
user2 = c(-0.009, -0.013, -0.01, -0.008, -0.008, -0.01, -0.005, -0.02, 0.28... | How to plot multiple users' deviations from predictions of bandwidth consumption over time? | For these particular data, I would make a line plot, like the following.
Here's the R code I used:
dat <- data.frame(user1 = c(-0.075, -0.09, 0.32, -0.242, -0.368, -0.401, -0.73, -0.367, -0.294, -0.0 | How to plot multiple users' deviations from predictions of bandwidth consumption over time?
For these particular data, I would make a line plot, like the following.
Here's the R code I used:
dat <- data.frame(user1 = c(-0.075, -0.09, 0.32, -0.242, -0.368, -0.401, -0.73, -0.367, -0.294, -0.043, 1.296, 0.075, -0.373),
... | How to plot multiple users' deviations from predictions of bandwidth consumption over time?
For these particular data, I would make a line plot, like the following.
Here's the R code I used:
dat <- data.frame(user1 = c(-0.075, -0.09, 0.32, -0.242, -0.368, -0.401, -0.73, -0.367, -0.294, -0.0 |
54,595 | How to define marked point processes? | A point process is a collection of random variables that are positions in some space (like locations on a plane). A marked point process is a point process in which some additional features are measured at each point.
For your situation, the locations of the points are by design rather than random, and so while you co... | How to define marked point processes? | A point process is a collection of random variables that are positions in some space (like locations on a plane). A marked point process is a point process in which some additional features are measu | How to define marked point processes?
A point process is a collection of random variables that are positions in some space (like locations on a plane). A marked point process is a point process in which some additional features are measured at each point.
For your situation, the locations of the points are by design r... | How to define marked point processes?
A point process is a collection of random variables that are positions in some space (like locations on a plane). A marked point process is a point process in which some additional features are measu |
54,596 | How to define marked point processes? | The aim of such cases is to model the measured characteristic in spatial locations. These type of data are called geostatistical data. So you have to apply the geostatistics theory as the approperiate methods for modeling geostatistical data. Then, the answer of your question is no, this approach could not to be a mark... | How to define marked point processes? | The aim of such cases is to model the measured characteristic in spatial locations. These type of data are called geostatistical data. So you have to apply the geostatistics theory as the approperiate | How to define marked point processes?
The aim of such cases is to model the measured characteristic in spatial locations. These type of data are called geostatistical data. So you have to apply the geostatistics theory as the approperiate methods for modeling geostatistical data. Then, the answer of your question is no... | How to define marked point processes?
The aim of such cases is to model the measured characteristic in spatial locations. These type of data are called geostatistical data. So you have to apply the geostatistics theory as the approperiate |
54,597 | Looking for estimates for my data using cumulative beta distribution | First remark : your data is nowhere near a distribution, and definitely not the beta function. As I see it, you see your boot.mean as 'density' and your x-axis (the index?) as value. The beta function is limited between 0 and 1, and as the area under the curve of any density function should equal 1, your data doesn't c... | Looking for estimates for my data using cumulative beta distribution | First remark : your data is nowhere near a distribution, and definitely not the beta function. As I see it, you see your boot.mean as 'density' and your x-axis (the index?) as value. The beta function | Looking for estimates for my data using cumulative beta distribution
First remark : your data is nowhere near a distribution, and definitely not the beta function. As I see it, you see your boot.mean as 'density' and your x-axis (the index?) as value. The beta function is limited between 0 and 1, and as the area under ... | Looking for estimates for my data using cumulative beta distribution
First remark : your data is nowhere near a distribution, and definitely not the beta function. As I see it, you see your boot.mean as 'density' and your x-axis (the index?) as value. The beta function |
54,598 | Looking for estimates for my data using cumulative beta distribution | It's not a good idea to rescale the data in this ad hoc way, because it can result in an inferior fit (and ruins any chance of estimating the sampling variance of the scale parameter): just fit a scaled Beta distribution to the data themselves.
You do have to assign percentage points to the data; below I have used $p(i... | Looking for estimates for my data using cumulative beta distribution | It's not a good idea to rescale the data in this ad hoc way, because it can result in an inferior fit (and ruins any chance of estimating the sampling variance of the scale parameter): just fit a scal | Looking for estimates for my data using cumulative beta distribution
It's not a good idea to rescale the data in this ad hoc way, because it can result in an inferior fit (and ruins any chance of estimating the sampling variance of the scale parameter): just fit a scaled Beta distribution to the data themselves.
You do... | Looking for estimates for my data using cumulative beta distribution
It's not a good idea to rescale the data in this ad hoc way, because it can result in an inferior fit (and ruins any chance of estimating the sampling variance of the scale parameter): just fit a scal |
54,599 | Whether to enter all predictors at once or perform a hierarchical regression? | Whether to use hierarchical regression or enter all predictors at once
As a starting point, the final block of a hierarchical regression is the same as if you had entered all predictors at once.
If you have an hypothesis that is aligned with hierarchical regression, then you should perform a hierarchical regression. Y... | Whether to enter all predictors at once or perform a hierarchical regression? | Whether to use hierarchical regression or enter all predictors at once
As a starting point, the final block of a hierarchical regression is the same as if you had entered all predictors at once.
If y | Whether to enter all predictors at once or perform a hierarchical regression?
Whether to use hierarchical regression or enter all predictors at once
As a starting point, the final block of a hierarchical regression is the same as if you had entered all predictors at once.
If you have an hypothesis that is aligned with... | Whether to enter all predictors at once or perform a hierarchical regression?
Whether to use hierarchical regression or enter all predictors at once
As a starting point, the final block of a hierarchical regression is the same as if you had entered all predictors at once.
If y |
54,600 | Whether to enter all predictors at once or perform a hierarchical regression? | The rule of thumb is 10 cases for each IV. You have (if I counted right) 11 IVs. Not too far over that. Surely the two SES variables are highly correlated? They could be combined and that gets you down to 10 IVs.
Regarding dependent vs. independent - you haven't said what your sampling plan is. You can do SNA and still... | Whether to enter all predictors at once or perform a hierarchical regression? | The rule of thumb is 10 cases for each IV. You have (if I counted right) 11 IVs. Not too far over that. Surely the two SES variables are highly correlated? They could be combined and that gets you dow | Whether to enter all predictors at once or perform a hierarchical regression?
The rule of thumb is 10 cases for each IV. You have (if I counted right) 11 IVs. Not too far over that. Surely the two SES variables are highly correlated? They could be combined and that gets you down to 10 IVs.
Regarding dependent vs. indep... | Whether to enter all predictors at once or perform a hierarchical regression?
The rule of thumb is 10 cases for each IV. You have (if I counted right) 11 IVs. Not too far over that. Surely the two SES variables are highly correlated? They could be combined and that gets you dow |
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