idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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2,201 | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | If you want to sample points uniformly distributed on the 3D sphere (i.e., the surface of a 3D ball), use a simple rejection, or the method of Marsaglia (Ann. Math. Statist., 43 (1972), pp. 645–646). For low dimensions, the rejection ratio is quite low.
If you want to generate random points from higher-dimensional sp... | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | If you want to sample points uniformly distributed on the 3D sphere (i.e., the surface of a 3D ball), use a simple rejection, or the method of Marsaglia (Ann. Math. Statist., 43 (1972), pp. 645–646) | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
If you want to sample points uniformly distributed on the 3D sphere (i.e., the surface of a 3D ball), use a simple rejection, or the method of Marsaglia (Ann. Math. Statist., 43 (1972), pp. 645–646). For low dimensions, the rejection ... | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
If you want to sample points uniformly distributed on the 3D sphere (i.e., the surface of a 3D ball), use a simple rejection, or the method of Marsaglia (Ann. Math. Statist., 43 (1972), pp. 645–646) |
2,202 | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | I had a similar problem (n-sphere) during my PhD and one of the local 'experts' suggested rejection sampling from a n-cube! This, of course, would have taken the age of the universe as I was looking at n in the order of hunderds.
The algorithm I ended up using is very simple and published in:
W.P. Petersen and A. Berna... | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | I had a similar problem (n-sphere) during my PhD and one of the local 'experts' suggested rejection sampling from a n-cube! This, of course, would have taken the age of the universe as I was looking a | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
I had a similar problem (n-sphere) during my PhD and one of the local 'experts' suggested rejection sampling from a n-cube! This, of course, would have taken the age of the universe as I was looking at n in the order of hunderds.
The al... | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
I had a similar problem (n-sphere) during my PhD and one of the local 'experts' suggested rejection sampling from a n-cube! This, of course, would have taken the age of the universe as I was looking a |
2,203 | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | Here is the pseudocode:
$v \sim MultiVariateGaussian(\mu,\sigma I)$
$v = \frac{v}{ \| v \| }$
In pytorch:
v = MultivariateNormal(torch.zeros(10000), torch.eye(10000))
v = v/v.norm(2)
I don't understand this well enough but I've been told by whuber that:
v = torch.normal(torch.zeros(10000), torch.eye(10000))
v = v... | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | Here is the pseudocode:
$v \sim MultiVariateGaussian(\mu,\sigma I)$
$v = \frac{v}{ \| v \| }$
In pytorch:
v = MultivariateNormal(torch.zeros(10000), torch.eye(10000))
v = v/v.norm(2)
I don't und | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
Here is the pseudocode:
$v \sim MultiVariateGaussian(\mu,\sigma I)$
$v = \frac{v}{ \| v \| }$
In pytorch:
v = MultivariateNormal(torch.zeros(10000), torch.eye(10000))
v = v/v.norm(2)
I don't understand this well enough but I've be... | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
Here is the pseudocode:
$v \sim MultiVariateGaussian(\mu,\sigma I)$
$v = \frac{v}{ \| v \| }$
In pytorch:
v = MultivariateNormal(torch.zeros(10000), torch.eye(10000))
v = v/v.norm(2)
I don't und |
2,204 | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | I have had this problem before, and here is an alternative I found,
As for the distribution itself,
the formula I found that works decently is to use polar coordinates (I actually use a variation of poler coordinates that developed), then convert to Cartesian coordinates.
The radius is of course the radius of the spher... | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | I have had this problem before, and here is an alternative I found,
As for the distribution itself,
the formula I found that works decently is to use polar coordinates (I actually use a variation of p | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
I have had this problem before, and here is an alternative I found,
As for the distribution itself,
the formula I found that works decently is to use polar coordinates (I actually use a variation of poler coordinates that developed), th... | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
I have had this problem before, and here is an alternative I found,
As for the distribution itself,
the formula I found that works decently is to use polar coordinates (I actually use a variation of p |
2,205 | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | My best guess would be to first generate a set of uniformly distributed points in 2 dimensional space and to then project those points onto the surface of a sphere using some sort of projection.
You will probably have to mix and match the way you generate the points with the way that you map them. In terms of the 2D p... | How to generate uniformly distributed points on the surface of the 3-d unit sphere? | My best guess would be to first generate a set of uniformly distributed points in 2 dimensional space and to then project those points onto the surface of a sphere using some sort of projection.
You | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
My best guess would be to first generate a set of uniformly distributed points in 2 dimensional space and to then project those points onto the surface of a sphere using some sort of projection.
You will probably have to mix and match ... | How to generate uniformly distributed points on the surface of the 3-d unit sphere?
My best guess would be to first generate a set of uniformly distributed points in 2 dimensional space and to then project those points onto the surface of a sphere using some sort of projection.
You |
2,206 | What is the difference between a "link function" and a "canonical link function" for GLM | The above answers are more intuitive, so I try more rigor.
What is a GLM?
Let $Y=(y,\mathbf{x})$ denote a set of a response $y$ and $p$-dimensional covariate vector $\mathbf{x}=(x_1,\dots,x_p)$ with expected value $E(y)=\mu$. For $i=1,\dots,n$ independent observations, the distribution of each $y_i$ is an exponential f... | What is the difference between a "link function" and a "canonical link function" for GLM | The above answers are more intuitive, so I try more rigor.
What is a GLM?
Let $Y=(y,\mathbf{x})$ denote a set of a response $y$ and $p$-dimensional covariate vector $\mathbf{x}=(x_1,\dots,x_p)$ with e | What is the difference between a "link function" and a "canonical link function" for GLM
The above answers are more intuitive, so I try more rigor.
What is a GLM?
Let $Y=(y,\mathbf{x})$ denote a set of a response $y$ and $p$-dimensional covariate vector $\mathbf{x}=(x_1,\dots,x_p)$ with expected value $E(y)=\mu$. For $... | What is the difference between a "link function" and a "canonical link function" for GLM
The above answers are more intuitive, so I try more rigor.
What is a GLM?
Let $Y=(y,\mathbf{x})$ denote a set of a response $y$ and $p$-dimensional covariate vector $\mathbf{x}=(x_1,\dots,x_p)$ with e |
2,207 | What is the difference between a "link function" and a "canonical link function" for GLM | Here is a little diagram inspired from MIT's 18.650 class which I find quite useful as it helps visualizing the relationships between these functions. I have used the same notation as in @momo's post:
$\gamma(\theta)$ is the cumulant moment generating function
$g(\mu)$ is the link function
So the link function $g$ ... | What is the difference between a "link function" and a "canonical link function" for GLM | Here is a little diagram inspired from MIT's 18.650 class which I find quite useful as it helps visualizing the relationships between these functions. I have used the same notation as in @momo's post: | What is the difference between a "link function" and a "canonical link function" for GLM
Here is a little diagram inspired from MIT's 18.650 class which I find quite useful as it helps visualizing the relationships between these functions. I have used the same notation as in @momo's post:
$\gamma(\theta)$ is the cum... | What is the difference between a "link function" and a "canonical link function" for GLM
Here is a little diagram inspired from MIT's 18.650 class which I find quite useful as it helps visualizing the relationships between these functions. I have used the same notation as in @momo's post: |
2,208 | What is the difference between a "link function" and a "canonical link function" for GLM | gung's quoted a good explanation: the canonical link possesses special theoretical properties of minimal sufficiency. This means that you can define a conditional logit model (which economists call a fixed effect model) by conditioning on the number of outcomes, but you cannot define a conditional probit model, because... | What is the difference between a "link function" and a "canonical link function" for GLM | gung's quoted a good explanation: the canonical link possesses special theoretical properties of minimal sufficiency. This means that you can define a conditional logit model (which economists call a | What is the difference between a "link function" and a "canonical link function" for GLM
gung's quoted a good explanation: the canonical link possesses special theoretical properties of minimal sufficiency. This means that you can define a conditional logit model (which economists call a fixed effect model) by conditio... | What is the difference between a "link function" and a "canonical link function" for GLM
gung's quoted a good explanation: the canonical link possesses special theoretical properties of minimal sufficiency. This means that you can define a conditional logit model (which economists call a |
2,209 | What is the difference between a "link function" and a "canonical link function" for GLM | The answers above have already covered what I want to say. Just to clarify a few points as a researcher of machine learning:
link function is nothing but the inverse of the activation function. For example, logit is the inverse of sigmoid, probit is the inverse of the cumulative distribution function of Gaussian.
If ... | What is the difference between a "link function" and a "canonical link function" for GLM | The answers above have already covered what I want to say. Just to clarify a few points as a researcher of machine learning:
link function is nothing but the inverse of the activation function. For | What is the difference between a "link function" and a "canonical link function" for GLM
The answers above have already covered what I want to say. Just to clarify a few points as a researcher of machine learning:
link function is nothing but the inverse of the activation function. For example, logit is the inverse o... | What is the difference between a "link function" and a "canonical link function" for GLM
The answers above have already covered what I want to say. Just to clarify a few points as a researcher of machine learning:
link function is nothing but the inverse of the activation function. For |
2,210 | Shape of confidence interval for predicted values in linear regression | I'll discuss it in intuitive terms.
Both confidence intervals and prediction intervals in regression take account of the fact that the intercept and slope are uncertain - you estimate the values from the data, but the population values may be different (if you took a new sample, you'd get different estimated values).
A... | Shape of confidence interval for predicted values in linear regression | I'll discuss it in intuitive terms.
Both confidence intervals and prediction intervals in regression take account of the fact that the intercept and slope are uncertain - you estimate the values from | Shape of confidence interval for predicted values in linear regression
I'll discuss it in intuitive terms.
Both confidence intervals and prediction intervals in regression take account of the fact that the intercept and slope are uncertain - you estimate the values from the data, but the population values may be differ... | Shape of confidence interval for predicted values in linear regression
I'll discuss it in intuitive terms.
Both confidence intervals and prediction intervals in regression take account of the fact that the intercept and slope are uncertain - you estimate the values from |
2,211 | Shape of confidence interval for predicted values in linear regression | The accepted answer brings indeed the necessary intuition. It only misses the visualization of combining both linear and angular uncertainties, which refers very nicely back to the plots in the question. So here it goes. Let's call a' and b' the uncertainties of a, and b, respectively, quantities commonly returned by a... | Shape of confidence interval for predicted values in linear regression | The accepted answer brings indeed the necessary intuition. It only misses the visualization of combining both linear and angular uncertainties, which refers very nicely back to the plots in the questi | Shape of confidence interval for predicted values in linear regression
The accepted answer brings indeed the necessary intuition. It only misses the visualization of combining both linear and angular uncertainties, which refers very nicely back to the plots in the question. So here it goes. Let's call a' and b' the unc... | Shape of confidence interval for predicted values in linear regression
The accepted answer brings indeed the necessary intuition. It only misses the visualization of combining both linear and angular uncertainties, which refers very nicely back to the plots in the questi |
2,212 | Shape of confidence interval for predicted values in linear regression | An even simpler answer is to look at the formula for the confidence interval around a certain point, x:
$$
\hat{y}_{ci}(x) = \hat{y} \pm t_{n-2, \alpha/2} \sqrt{\frac{\Sigma_{i=1}^n (y_i-\hat{y})^2}{n-2}} \sqrt{\frac{1}{n} + \frac{(x-\bar{x})^2}{\Sigma_{i=1}^n (x_i-\bar{x})^2}}
$$
We can see that in the second squarero... | Shape of confidence interval for predicted values in linear regression | An even simpler answer is to look at the formula for the confidence interval around a certain point, x:
$$
\hat{y}_{ci}(x) = \hat{y} \pm t_{n-2, \alpha/2} \sqrt{\frac{\Sigma_{i=1}^n (y_i-\hat{y})^2}{n | Shape of confidence interval for predicted values in linear regression
An even simpler answer is to look at the formula for the confidence interval around a certain point, x:
$$
\hat{y}_{ci}(x) = \hat{y} \pm t_{n-2, \alpha/2} \sqrt{\frac{\Sigma_{i=1}^n (y_i-\hat{y})^2}{n-2}} \sqrt{\frac{1}{n} + \frac{(x-\bar{x})^2}{\Si... | Shape of confidence interval for predicted values in linear regression
An even simpler answer is to look at the formula for the confidence interval around a certain point, x:
$$
\hat{y}_{ci}(x) = \hat{y} \pm t_{n-2, \alpha/2} \sqrt{\frac{\Sigma_{i=1}^n (y_i-\hat{y})^2}{n |
2,213 | How to efficiently manage a statistical analysis project? | I am compiling a quick series of guidelines I found on SO (as suggested by @Shane), Biostar (hereafter, BS), and this SE. I tried my best to acknowledge ownership for each item, and to select first or highly upvoted answer. I also added things of my own, and flagged items that are specific to the [R] environment.
Data ... | How to efficiently manage a statistical analysis project? | I am compiling a quick series of guidelines I found on SO (as suggested by @Shane), Biostar (hereafter, BS), and this SE. I tried my best to acknowledge ownership for each item, and to select first or | How to efficiently manage a statistical analysis project?
I am compiling a quick series of guidelines I found on SO (as suggested by @Shane), Biostar (hereafter, BS), and this SE. I tried my best to acknowledge ownership for each item, and to select first or highly upvoted answer. I also added things of my own, and fla... | How to efficiently manage a statistical analysis project?
I am compiling a quick series of guidelines I found on SO (as suggested by @Shane), Biostar (hereafter, BS), and this SE. I tried my best to acknowledge ownership for each item, and to select first or |
2,214 | How to efficiently manage a statistical analysis project? | This doesn't specifically provide an answer, but you may want to look at these related stackoverflow questions:
"Workflow for statistical analysis and report writing"
"Organizing R Source Code"
"How to organize large R programs?"
"R and version control for the solo data analyst"
"How does software development compare... | How to efficiently manage a statistical analysis project? | This doesn't specifically provide an answer, but you may want to look at these related stackoverflow questions:
"Workflow for statistical analysis and report writing"
"Organizing R Source Code"
"How | How to efficiently manage a statistical analysis project?
This doesn't specifically provide an answer, but you may want to look at these related stackoverflow questions:
"Workflow for statistical analysis and report writing"
"Organizing R Source Code"
"How to organize large R programs?"
"R and version control for the... | How to efficiently manage a statistical analysis project?
This doesn't specifically provide an answer, but you may want to look at these related stackoverflow questions:
"Workflow for statistical analysis and report writing"
"Organizing R Source Code"
"How |
2,215 | How to efficiently manage a statistical analysis project? | This overlaps with Shane's answer, but in my view there are two main piers:
Reproducibility; not
only because you won't end with
results that are made "somehow" but
also be able to rerun the analysis
faster (on other data or with
slightly changed parameters) and have
more time to think about the results. For a huge da... | How to efficiently manage a statistical analysis project? | This overlaps with Shane's answer, but in my view there are two main piers:
Reproducibility; not
only because you won't end with
results that are made "somehow" but
also be able to rerun the analysis | How to efficiently manage a statistical analysis project?
This overlaps with Shane's answer, but in my view there are two main piers:
Reproducibility; not
only because you won't end with
results that are made "somehow" but
also be able to rerun the analysis
faster (on other data or with
slightly changed parameters) an... | How to efficiently manage a statistical analysis project?
This overlaps with Shane's answer, but in my view there are two main piers:
Reproducibility; not
only because you won't end with
results that are made "somehow" but
also be able to rerun the analysis |
2,216 | How to efficiently manage a statistical analysis project? | van Belle is the source for the rules of successful statistical projects. | How to efficiently manage a statistical analysis project? | van Belle is the source for the rules of successful statistical projects. | How to efficiently manage a statistical analysis project?
van Belle is the source for the rules of successful statistical projects. | How to efficiently manage a statistical analysis project?
van Belle is the source for the rules of successful statistical projects. |
2,217 | How to efficiently manage a statistical analysis project? | Just my 2 cents. I've found Notepad++ useful for this. I can maintain separate scripts (program control, data formatting, etc.) and a .pad file for each project. The .pad file call's all the scripts associated with that project. | How to efficiently manage a statistical analysis project? | Just my 2 cents. I've found Notepad++ useful for this. I can maintain separate scripts (program control, data formatting, etc.) and a .pad file for each project. The .pad file call's all the scripts | How to efficiently manage a statistical analysis project?
Just my 2 cents. I've found Notepad++ useful for this. I can maintain separate scripts (program control, data formatting, etc.) and a .pad file for each project. The .pad file call's all the scripts associated with that project. | How to efficiently manage a statistical analysis project?
Just my 2 cents. I've found Notepad++ useful for this. I can maintain separate scripts (program control, data formatting, etc.) and a .pad file for each project. The .pad file call's all the scripts |
2,218 | How to efficiently manage a statistical analysis project? | While the other answers are great, I would add another sentiment: Avoid using SPSS. I used SPSS for my master's thesis and now on my regular job in market research.
While working with SPSS, it was incredibly hard to develop organized statistical code, due to the fact that SPSS is bad at handling multiple files (sure, ... | How to efficiently manage a statistical analysis project? | While the other answers are great, I would add another sentiment: Avoid using SPSS. I used SPSS for my master's thesis and now on my regular job in market research.
While working with SPSS, it was in | How to efficiently manage a statistical analysis project?
While the other answers are great, I would add another sentiment: Avoid using SPSS. I used SPSS for my master's thesis and now on my regular job in market research.
While working with SPSS, it was incredibly hard to develop organized statistical code, due to th... | How to efficiently manage a statistical analysis project?
While the other answers are great, I would add another sentiment: Avoid using SPSS. I used SPSS for my master's thesis and now on my regular job in market research.
While working with SPSS, it was in |
2,219 | How to efficiently manage a statistical analysis project? | Jupyter Notebooks, which work with R/Python/Matlab/etc, remove the hassle of remembering which script generates a certain figure. This post describes a tidy way of keeping the code and the figure right beside each other. Keeping all figures for a paper or thesis chapter in a single notebook makes the asccoiated code ve... | How to efficiently manage a statistical analysis project? | Jupyter Notebooks, which work with R/Python/Matlab/etc, remove the hassle of remembering which script generates a certain figure. This post describes a tidy way of keeping the code and the figure righ | How to efficiently manage a statistical analysis project?
Jupyter Notebooks, which work with R/Python/Matlab/etc, remove the hassle of remembering which script generates a certain figure. This post describes a tidy way of keeping the code and the figure right beside each other. Keeping all figures for a paper or thesis... | How to efficiently manage a statistical analysis project?
Jupyter Notebooks, which work with R/Python/Matlab/etc, remove the hassle of remembering which script generates a certain figure. This post describes a tidy way of keeping the code and the figure righ |
2,220 | Feature selection for "final" model when performing cross-validation in machine learning | Whether you use LOO or K-fold CV, you'll end up with different features since the cross-validation iteration must be the most outer loop, as you said. You can think of some kind of voting scheme which would rate the n-vectors of features you got from your LOO-CV (can't remember the paper but it is worth checking the wo... | Feature selection for "final" model when performing cross-validation in machine learning | Whether you use LOO or K-fold CV, you'll end up with different features since the cross-validation iteration must be the most outer loop, as you said. You can think of some kind of voting scheme which | Feature selection for "final" model when performing cross-validation in machine learning
Whether you use LOO or K-fold CV, you'll end up with different features since the cross-validation iteration must be the most outer loop, as you said. You can think of some kind of voting scheme which would rate the n-vectors of fe... | Feature selection for "final" model when performing cross-validation in machine learning
Whether you use LOO or K-fold CV, you'll end up with different features since the cross-validation iteration must be the most outer loop, as you said. You can think of some kind of voting scheme which |
2,221 | Feature selection for "final" model when performing cross-validation in machine learning | In principle:
Make your predictions using a single model trained on the entire dataset (so there is only one set of features). The cross-validation is only used to estimate the predictive performance of the single model trained on the whole dataset. It is VITAL in using cross-validation that in each fold you repeat t... | Feature selection for "final" model when performing cross-validation in machine learning | In principle:
Make your predictions using a single model trained on the entire dataset (so there is only one set of features). The cross-validation is only used to estimate the predictive performance | Feature selection for "final" model when performing cross-validation in machine learning
In principle:
Make your predictions using a single model trained on the entire dataset (so there is only one set of features). The cross-validation is only used to estimate the predictive performance of the single model trained on... | Feature selection for "final" model when performing cross-validation in machine learning
In principle:
Make your predictions using a single model trained on the entire dataset (so there is only one set of features). The cross-validation is only used to estimate the predictive performance |
2,222 | Feature selection for "final" model when performing cross-validation in machine learning | To add to chl: When using support vector machines, a highly recommended penalization method is the elastic net. This method will shrink coefficients towards zero, and in theory retains the most stable coefficients in the model. Initially it was used in a regression framework, but it is easily extended for use with supp... | Feature selection for "final" model when performing cross-validation in machine learning | To add to chl: When using support vector machines, a highly recommended penalization method is the elastic net. This method will shrink coefficients towards zero, and in theory retains the most stable | Feature selection for "final" model when performing cross-validation in machine learning
To add to chl: When using support vector machines, a highly recommended penalization method is the elastic net. This method will shrink coefficients towards zero, and in theory retains the most stable coefficients in the model. Ini... | Feature selection for "final" model when performing cross-validation in machine learning
To add to chl: When using support vector machines, a highly recommended penalization method is the elastic net. This method will shrink coefficients towards zero, and in theory retains the most stable |
2,223 | Feature selection for "final" model when performing cross-validation in machine learning | As step 6 (or 0) you run the feature detection algorithm on the entire data set.
The logic is the following: you have to think of cross-validation as a method for finding out the properties of the procedure you are using to select the features. It answers the question: "if I have some data and perform this procedure, t... | Feature selection for "final" model when performing cross-validation in machine learning | As step 6 (or 0) you run the feature detection algorithm on the entire data set.
The logic is the following: you have to think of cross-validation as a method for finding out the properties of the pro | Feature selection for "final" model when performing cross-validation in machine learning
As step 6 (or 0) you run the feature detection algorithm on the entire data set.
The logic is the following: you have to think of cross-validation as a method for finding out the properties of the procedure you are using to select ... | Feature selection for "final" model when performing cross-validation in machine learning
As step 6 (or 0) you run the feature detection algorithm on the entire data set.
The logic is the following: you have to think of cross-validation as a method for finding out the properties of the pro |
2,224 | Feature selection for "final" model when performing cross-validation in machine learning | This is how I select features. Suppose based on certain knowledge, there are 2 models to be compared. Model A uses features no.1 to no. 10. Model B uses no.11 to no. 20. I will apply LOO CV to model A to get its out-of-sample performance. Do the same to model B and then compare them. | Feature selection for "final" model when performing cross-validation in machine learning | This is how I select features. Suppose based on certain knowledge, there are 2 models to be compared. Model A uses features no.1 to no. 10. Model B uses no.11 to no. 20. I will apply LOO CV to model A | Feature selection for "final" model when performing cross-validation in machine learning
This is how I select features. Suppose based on certain knowledge, there are 2 models to be compared. Model A uses features no.1 to no. 10. Model B uses no.11 to no. 20. I will apply LOO CV to model A to get its out-of-sample perfo... | Feature selection for "final" model when performing cross-validation in machine learning
This is how I select features. Suppose based on certain knowledge, there are 2 models to be compared. Model A uses features no.1 to no. 10. Model B uses no.11 to no. 20. I will apply LOO CV to model A |
2,225 | Feature selection for "final" model when performing cross-validation in machine learning | I'm not sure about classification problems, but in the case of feature selection for regression problems, Jun Shao showed that Leave-One-Out CV is asymptotically inconsistent, i.e. the probability of selecting the proper subset of features does not converge to 1 as the number of samples increases. From a practical poin... | Feature selection for "final" model when performing cross-validation in machine learning | I'm not sure about classification problems, but in the case of feature selection for regression problems, Jun Shao showed that Leave-One-Out CV is asymptotically inconsistent, i.e. the probability of | Feature selection for "final" model when performing cross-validation in machine learning
I'm not sure about classification problems, but in the case of feature selection for regression problems, Jun Shao showed that Leave-One-Out CV is asymptotically inconsistent, i.e. the probability of selecting the proper subset of ... | Feature selection for "final" model when performing cross-validation in machine learning
I'm not sure about classification problems, but in the case of feature selection for regression problems, Jun Shao showed that Leave-One-Out CV is asymptotically inconsistent, i.e. the probability of |
2,226 | What do the residuals in a logistic regression mean? | The easiest residuals to understand are the deviance residuals as when squared these sum to -2 times the log-likelihood. In its simplest terms logistic regression can be understood in terms of fitting the function $p = \text{logit}^{-1}(X\beta)$ for known $X$ in such a way as to minimise the total deviance, which is th... | What do the residuals in a logistic regression mean? | The easiest residuals to understand are the deviance residuals as when squared these sum to -2 times the log-likelihood. In its simplest terms logistic regression can be understood in terms of fitting | What do the residuals in a logistic regression mean?
The easiest residuals to understand are the deviance residuals as when squared these sum to -2 times the log-likelihood. In its simplest terms logistic regression can be understood in terms of fitting the function $p = \text{logit}^{-1}(X\beta)$ for known $X$ in such... | What do the residuals in a logistic regression mean?
The easiest residuals to understand are the deviance residuals as when squared these sum to -2 times the log-likelihood. In its simplest terms logistic regression can be understood in terms of fitting |
2,227 | What do the residuals in a logistic regression mean? | Response:
$$y_i - \hat\mu_i$$
response residuals are inadequate for assessing a fitted glm, because GLMs are based on distributions where (in general) the variance depends on the mean.
Pearson:
The most direct way to handle the non-constant variance is to divide
it out:
$$ \frac{y_i - \hat\mu_i}{\sqrt{V(\mu_i)|_{\hat\m... | What do the residuals in a logistic regression mean? | Response:
$$y_i - \hat\mu_i$$
response residuals are inadequate for assessing a fitted glm, because GLMs are based on distributions where (in general) the variance depends on the mean.
Pearson:
The mo | What do the residuals in a logistic regression mean?
Response:
$$y_i - \hat\mu_i$$
response residuals are inadequate for assessing a fitted glm, because GLMs are based on distributions where (in general) the variance depends on the mean.
Pearson:
The most direct way to handle the non-constant variance is to divide
it o... | What do the residuals in a logistic regression mean?
Response:
$$y_i - \hat\mu_i$$
response residuals are inadequate for assessing a fitted glm, because GLMs are based on distributions where (in general) the variance depends on the mean.
Pearson:
The mo |
2,228 | What do the residuals in a logistic regression mean? | On Pearsons residuals,
The Pearson residual is the difference between the observed and estimated probabilities divided by the binomial standard deviation of the estimated probability. Therefore standardizing the residuals.
For large samples the standardized residuals should have a normal distribution.
From Menard, Scot... | What do the residuals in a logistic regression mean? | On Pearsons residuals,
The Pearson residual is the difference between the observed and estimated probabilities divided by the binomial standard deviation of the estimated probability. Therefore standa | What do the residuals in a logistic regression mean?
On Pearsons residuals,
The Pearson residual is the difference between the observed and estimated probabilities divided by the binomial standard deviation of the estimated probability. Therefore standardizing the residuals.
For large samples the standardized residuals... | What do the residuals in a logistic regression mean?
On Pearsons residuals,
The Pearson residual is the difference between the observed and estimated probabilities divided by the binomial standard deviation of the estimated probability. Therefore standa |
2,229 | What do the residuals in a logistic regression mean? | The working residuals are the residuals in the final iteration of any iteratively weighted least squares method. I reckon that means the residuals when we think its the last iteration of our running of model. That can give rise to discussion that model running is an iterative exercise. | What do the residuals in a logistic regression mean? | The working residuals are the residuals in the final iteration of any iteratively weighted least squares method. I reckon that means the residuals when we think its the last iteration of our running o | What do the residuals in a logistic regression mean?
The working residuals are the residuals in the final iteration of any iteratively weighted least squares method. I reckon that means the residuals when we think its the last iteration of our running of model. That can give rise to discussion that model running is an ... | What do the residuals in a logistic regression mean?
The working residuals are the residuals in the final iteration of any iteratively weighted least squares method. I reckon that means the residuals when we think its the last iteration of our running o |
2,230 | What do the residuals in a logistic regression mean? | Pearson Residuals
As tosonb1 points out, "The Pearson residual is the difference between the observed and estimated probabilities divided by the binomial standard deviation of the estimated probability".
I just wanted to mention that Pearson residual is mostly useful with grouped data i.e, say, there are $n_i$ trials... | What do the residuals in a logistic regression mean? | Pearson Residuals
As tosonb1 points out, "The Pearson residual is the difference between the observed and estimated probabilities divided by the binomial standard deviation of the estimated probabili | What do the residuals in a logistic regression mean?
Pearson Residuals
As tosonb1 points out, "The Pearson residual is the difference between the observed and estimated probabilities divided by the binomial standard deviation of the estimated probability".
I just wanted to mention that Pearson residual is mostly usef... | What do the residuals in a logistic regression mean?
Pearson Residuals
As tosonb1 points out, "The Pearson residual is the difference between the observed and estimated probabilities divided by the binomial standard deviation of the estimated probabili |
2,231 | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2? | The analysis is complicated by the prospect that the game goes into "overtime" in order to win by a margin of at least two points. (Otherwise it would be as simple as the solution shown at https://stats.stackexchange.com/a/327015/919.) I will show how to visualize the problem and use that to break it down into readil... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w | The analysis is complicated by the prospect that the game goes into "overtime" in order to win by a margin of at least two points. (Otherwise it would be as simple as the solution shown at https://st | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2?
The analysis is complicated by the prospect that the game goes into "overtime" in order to win by a margin of at least two points. (Otherwise it would be as simple as the solution shown at https://stats.stackex... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w
The analysis is complicated by the prospect that the game goes into "overtime" in order to win by a margin of at least two points. (Otherwise it would be as simple as the solution shown at https://st |
2,232 | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2? | Using the binomial distribution and assuming every point is independent:
The probability the $58\%$ player gets to $21$ in the first $40$ points (taking account of the fact the last point must be won) is $\sum_{n=21}^{40} {n-1 \choose 20} 0.58^{21}0.42^{n-21}$ $=\sum_{k=21}^{40} {40 \choose k} 0.58^{k}0.42^{40-k}$ $\a... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w | Using the binomial distribution and assuming every point is independent:
The probability the $58\%$ player gets to $21$ in the first $40$ points (taking account of the fact the last point must be won | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2?
Using the binomial distribution and assuming every point is independent:
The probability the $58\%$ player gets to $21$ in the first $40$ points (taking account of the fact the last point must be won) is $\sum_... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w
Using the binomial distribution and assuming every point is independent:
The probability the $58\%$ player gets to $21$ in the first $40$ points (taking account of the fact the last point must be won |
2,233 | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2? | I went with a computational answer. Here is an R function that simulates a ping-pong game where the winner has to win by 2. The only argument is the probability that you win a point. It will return the final score of that game:
## data simulation function ----------------------------------------------------
sim_game <-... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w | I went with a computational answer. Here is an R function that simulates a ping-pong game where the winner has to win by 2. The only argument is the probability that you win a point. It will return th | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2?
I went with a computational answer. Here is an R function that simulates a ping-pong game where the winner has to win by 2. The only argument is the probability that you win a point. It will return the final sco... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w
I went with a computational answer. Here is an R function that simulates a ping-pong game where the winner has to win by 2. The only argument is the probability that you win a point. It will return th |
2,234 | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2? | Should we assume that the 58% chance of winning is fixed and that points are independent?
I believe that Whuber's answer is a good one, and beautifully written and explained, when the consideration is that every point is independent from the next one. However I believe that, in practice it is only an interesting starti... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w | Should we assume that the 58% chance of winning is fixed and that points are independent?
I believe that Whuber's answer is a good one, and beautifully written and explained, when the consideration is | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2?
Should we assume that the 58% chance of winning is fixed and that points are independent?
I believe that Whuber's answer is a good one, and beautifully written and explained, when the consideration is that every... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w
Should we assume that the 58% chance of winning is fixed and that points are independent?
I believe that Whuber's answer is a good one, and beautifully written and explained, when the consideration is |
2,235 | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2? | Much effort could be spent on a perfect model. But sometimes a bad model is better. And nothing says bad model like the central limit theorem -- everything is a normal curve.
We'll ignore "overtime". We'll model the sum of individual points as a normal curve. We'll model playing 38 rounds and whomever has the most ... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w | Much effort could be spent on a perfect model. But sometimes a bad model is better. And nothing says bad model like the central limit theorem -- everything is a normal curve.
We'll ignore "overtime" | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2?
Much effort could be spent on a perfect model. But sometimes a bad model is better. And nothing says bad model like the central limit theorem -- everything is a normal curve.
We'll ignore "overtime". We'll mo... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w
Much effort could be spent on a perfect model. But sometimes a bad model is better. And nothing says bad model like the central limit theorem -- everything is a normal curve.
We'll ignore "overtime" |
2,236 | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2? | Based on simulation, it looks like the probability of winning any given game is about 85.5%.
The probability of winning by exactly 2 (which is how I read the title, but doesn't seem to be what you're asking) is about 10.1%.
Run the code below.
set.seed(328409)
sim.game <- function(p)
{
x1 = 0
x2 = 0
while( (max... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w | Based on simulation, it looks like the probability of winning any given game is about 85.5%.
The probability of winning by exactly 2 (which is how I read the title, but doesn't seem to be what you're | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, win by 2?
Based on simulation, it looks like the probability of winning any given game is about 85.5%.
The probability of winning by exactly 2 (which is how I read the title, but doesn't seem to be what you're asking) is... | If I have a 58% chance of winning a point, what's the chance of me winning a ping pong game to 21, w
Based on simulation, it looks like the probability of winning any given game is about 85.5%.
The probability of winning by exactly 2 (which is how I read the title, but doesn't seem to be what you're |
2,237 | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to infinity? | Since you ask for insights, I'm going to take a fairly intuitive approach rather than a more mathematical tack:
Following the concepts in my answer here, we can formulate a ridge regression as a regression with dummy data by adding $p$ (in your formulation) observations, where $y_{n+j}=0$, $x_{j,n+j}=\sqrt{\lambda}$ a... | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to in | Since you ask for insights, I'm going to take a fairly intuitive approach rather than a more mathematical tack:
Following the concepts in my answer here, we can formulate a ridge regression as a regr | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to infinity?
Since you ask for insights, I'm going to take a fairly intuitive approach rather than a more mathematical tack:
Following the concepts in my answer here, we can formulate a ridge regression as a regression with ... | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to in
Since you ask for insights, I'm going to take a fairly intuitive approach rather than a more mathematical tack:
Following the concepts in my answer here, we can formulate a ridge regression as a regr |
2,238 | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to infinity? | If $\lambda \rightarrow \infty$ then our penalty term will be infinite for any $\beta$ other than $\beta = 0$, so that's the one we'll get. There is no other vector that will give us a finite value of the objective function.
(Update: Please see Glen_b's answer. This is not the correct historical reason!)
This comes f... | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to in | If $\lambda \rightarrow \infty$ then our penalty term will be infinite for any $\beta$ other than $\beta = 0$, so that's the one we'll get. There is no other vector that will give us a finite value of | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to infinity?
If $\lambda \rightarrow \infty$ then our penalty term will be infinite for any $\beta$ other than $\beta = 0$, so that's the one we'll get. There is no other vector that will give us a finite value of the objecti... | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to in
If $\lambda \rightarrow \infty$ then our penalty term will be infinite for any $\beta$ other than $\beta = 0$, so that's the one we'll get. There is no other vector that will give us a finite value of |
2,239 | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to infinity? | why is the term called Ridge Regression?
From Ridge Regression: Biased Estimation for Nonorthogonal Problems (1970):
A. E. Hoerl first suggested in 1962 [9] [11] that to control the inflation and
general instability associated with the least squares estimates, one can use
$$\beta^* = [X'X + kI]^{-1}X'Y; k \geq 0 (2.1... | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to in | why is the term called Ridge Regression?
From Ridge Regression: Biased Estimation for Nonorthogonal Problems (1970):
A. E. Hoerl first suggested in 1962 [9] [11] that to control the inflation and
ge | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to infinity?
why is the term called Ridge Regression?
From Ridge Regression: Biased Estimation for Nonorthogonal Problems (1970):
A. E. Hoerl first suggested in 1962 [9] [11] that to control the inflation and
general instab... | Why is ridge regression called "ridge", why is it needed, and what happens when $\lambda$ goes to in
why is the term called Ridge Regression?
From Ridge Regression: Biased Estimation for Nonorthogonal Problems (1970):
A. E. Hoerl first suggested in 1962 [9] [11] that to control the inflation and
ge |
2,240 | What are principal component scores? | First, let's define a score.
John, Mike and Kate get the following percentages for exams in Maths, Science, English and Music as follows:
Maths Science English Music
John 80 85 60 55
Mike 90 85 70 45
Kate 95 80 40 50
In this case... | What are principal component scores? | First, let's define a score.
John, Mike and Kate get the following percentages for exams in Maths, Science, English and Music as follows:
Maths Science English Music
John 80 | What are principal component scores?
First, let's define a score.
John, Mike and Kate get the following percentages for exams in Maths, Science, English and Music as follows:
Maths Science English Music
John 80 85 60 55
Mike 90 85 70 45
Kate 95 ... | What are principal component scores?
First, let's define a score.
John, Mike and Kate get the following percentages for exams in Maths, Science, English and Music as follows:
Maths Science English Music
John 80 |
2,241 | What are principal component scores? | Principal component analysis (PCA) is one popular approach analyzing variance when you are dealing with multivariate data. You have random variables X1, X2,...Xn which are all correlated (positively or negatively) to varying degrees, and you want to get a better understanding of what's going on. PCA can help.
What PCA ... | What are principal component scores? | Principal component analysis (PCA) is one popular approach analyzing variance when you are dealing with multivariate data. You have random variables X1, X2,...Xn which are all correlated (positively o | What are principal component scores?
Principal component analysis (PCA) is one popular approach analyzing variance when you are dealing with multivariate data. You have random variables X1, X2,...Xn which are all correlated (positively or negatively) to varying degrees, and you want to get a better understanding of wha... | What are principal component scores?
Principal component analysis (PCA) is one popular approach analyzing variance when you are dealing with multivariate data. You have random variables X1, X2,...Xn which are all correlated (positively o |
2,242 | What are principal component scores? | I like to think of principal component scores as "basically meaningless" until you actually give them some meaning. Interpretting PC scores in terms of "reality" is a tricky business - and there can really be no unique way to do it. It depends on what you know about the particular variables that are going into the PC... | What are principal component scores? | I like to think of principal component scores as "basically meaningless" until you actually give them some meaning. Interpretting PC scores in terms of "reality" is a tricky business - and there can | What are principal component scores?
I like to think of principal component scores as "basically meaningless" until you actually give them some meaning. Interpretting PC scores in terms of "reality" is a tricky business - and there can really be no unique way to do it. It depends on what you know about the particular... | What are principal component scores?
I like to think of principal component scores as "basically meaningless" until you actually give them some meaning. Interpretting PC scores in terms of "reality" is a tricky business - and there can |
2,243 | What are principal component scores? | Say you have a cloud of N points in, say, 3D (which can be listed in a 100x3 array). Then, the principal components analysis (PCA) fits an arbitrarily oriented ellipsoid into the data. The principal component score is the length of the diameters of the ellipsoid.
In the direction in which the diameter is large, the da... | What are principal component scores? | Say you have a cloud of N points in, say, 3D (which can be listed in a 100x3 array). Then, the principal components analysis (PCA) fits an arbitrarily oriented ellipsoid into the data. The principal c | What are principal component scores?
Say you have a cloud of N points in, say, 3D (which can be listed in a 100x3 array). Then, the principal components analysis (PCA) fits an arbitrarily oriented ellipsoid into the data. The principal component score is the length of the diameters of the ellipsoid.
In the direction i... | What are principal component scores?
Say you have a cloud of N points in, say, 3D (which can be listed in a 100x3 array). Then, the principal components analysis (PCA) fits an arbitrarily oriented ellipsoid into the data. The principal c |
2,244 | What are principal component scores? | The principal components of a data matrix are the eigenvector-eigenvalue pairs of its variance-covariance matrix. In essence, they are the decorrelated pieces of the variance. Each one is a linear combination of the variables for an observation -- suppose you measure w, x, y,z on each of a bunch of subjects. Your fi... | What are principal component scores? | The principal components of a data matrix are the eigenvector-eigenvalue pairs of its variance-covariance matrix. In essence, they are the decorrelated pieces of the variance. Each one is a linear c | What are principal component scores?
The principal components of a data matrix are the eigenvector-eigenvalue pairs of its variance-covariance matrix. In essence, they are the decorrelated pieces of the variance. Each one is a linear combination of the variables for an observation -- suppose you measure w, x, y,z on ... | What are principal component scores?
The principal components of a data matrix are the eigenvector-eigenvalue pairs of its variance-covariance matrix. In essence, they are the decorrelated pieces of the variance. Each one is a linear c |
2,245 | What are principal component scores? | Let $i=1,\dots,N$ index the rows and $j=1,\dots,M$ index the columns. Suppose you linearize the combination of variables (columns):
$$Z_{i,1} = c_{i,1}\cdot Y_{i,1} + c_{i,2}\cdot Y_{i,2} + ... + c_{i,M}\cdot Y_{i,M}$$
The above formula basically says to multiply row elements with a certain value $c$ (loadings) and sum... | What are principal component scores? | Let $i=1,\dots,N$ index the rows and $j=1,\dots,M$ index the columns. Suppose you linearize the combination of variables (columns):
$$Z_{i,1} = c_{i,1}\cdot Y_{i,1} + c_{i,2}\cdot Y_{i,2} + ... + c_{i | What are principal component scores?
Let $i=1,\dots,N$ index the rows and $j=1,\dots,M$ index the columns. Suppose you linearize the combination of variables (columns):
$$Z_{i,1} = c_{i,1}\cdot Y_{i,1} + c_{i,2}\cdot Y_{i,2} + ... + c_{i,M}\cdot Y_{i,M}$$
The above formula basically says to multiply row elements with a... | What are principal component scores?
Let $i=1,\dots,N$ index the rows and $j=1,\dots,M$ index the columns. Suppose you linearize the combination of variables (columns):
$$Z_{i,1} = c_{i,1}\cdot Y_{i,1} + c_{i,2}\cdot Y_{i,2} + ... + c_{i |
2,246 | What are principal component scores? | Principal component scores are a group of scores that are obtained following a Principle Components Analysis (PCA). In PCA the relationships between a group of scores is analyzed such that an equal number of new "imaginary" variables (aka principle components) are created. The first of these new imaginary variables i... | What are principal component scores? | Principal component scores are a group of scores that are obtained following a Principle Components Analysis (PCA). In PCA the relationships between a group of scores is analyzed such that an equal n | What are principal component scores?
Principal component scores are a group of scores that are obtained following a Principle Components Analysis (PCA). In PCA the relationships between a group of scores is analyzed such that an equal number of new "imaginary" variables (aka principle components) are created. The fir... | What are principal component scores?
Principal component scores are a group of scores that are obtained following a Principle Components Analysis (PCA). In PCA the relationships between a group of scores is analyzed such that an equal n |
2,247 | How to apply Neural Network to time series forecasting? | Here is a simple recipe that may help you get started writing code and testing ideas...
Let's assume you have monthly data recorded over several years, so you have 36 values. Let's also assume that you only care about predicting one month (value) in advance.
Exploratory data analysis: Apply some of the traditional tim... | How to apply Neural Network to time series forecasting? | Here is a simple recipe that may help you get started writing code and testing ideas...
Let's assume you have monthly data recorded over several years, so you have 36 values. Let's also assume that yo | How to apply Neural Network to time series forecasting?
Here is a simple recipe that may help you get started writing code and testing ideas...
Let's assume you have monthly data recorded over several years, so you have 36 values. Let's also assume that you only care about predicting one month (value) in advance.
Expl... | How to apply Neural Network to time series forecasting?
Here is a simple recipe that may help you get started writing code and testing ideas...
Let's assume you have monthly data recorded over several years, so you have 36 values. Let's also assume that yo |
2,248 | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math | Very quickly, I would say: 'multiple' applies to the number of predictors that enter the model (or equivalently the design matrix) with a single outcome (Y response), while 'multivariate' refers to a matrix of response vectors. Cannot remember the author who starts its introductory section on multivariate modeling with... | Explain the difference between multiple regression and multivariate regression, with minimal use of | Very quickly, I would say: 'multiple' applies to the number of predictors that enter the model (or equivalently the design matrix) with a single outcome (Y response), while 'multivariate' refers to a | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math
Very quickly, I would say: 'multiple' applies to the number of predictors that enter the model (or equivalently the design matrix) with a single outcome (Y response), while 'multivariate' refers to a matrix ... | Explain the difference between multiple regression and multivariate regression, with minimal use of
Very quickly, I would say: 'multiple' applies to the number of predictors that enter the model (or equivalently the design matrix) with a single outcome (Y response), while 'multivariate' refers to a |
2,249 | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math | Here are two closely related examples which illustrate the ideas. The examples are somewhat US centric but the ideas can be extrapolated to other countries.
Example 1
Suppose that a university wishes to refine its admission criteria so that they admit 'better' students. Also, suppose that a student's grade Point Averag... | Explain the difference between multiple regression and multivariate regression, with minimal use of | Here are two closely related examples which illustrate the ideas. The examples are somewhat US centric but the ideas can be extrapolated to other countries.
Example 1
Suppose that a university wishes | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math
Here are two closely related examples which illustrate the ideas. The examples are somewhat US centric but the ideas can be extrapolated to other countries.
Example 1
Suppose that a university wishes to refi... | Explain the difference between multiple regression and multivariate regression, with minimal use of
Here are two closely related examples which illustrate the ideas. The examples are somewhat US centric but the ideas can be extrapolated to other countries.
Example 1
Suppose that a university wishes |
2,250 | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math | Simple regression pertains to one dependent variable ($y$) and one independent variable ($x$): $y = f(x)$
Multiple regression (aka multivariable regression) pertains to one dependent variable and multiple independent variables: $y = f(x_1, x_2, ..., x_n)$
Multivariate regression pertains to multiple dependent variables... | Explain the difference between multiple regression and multivariate regression, with minimal use of | Simple regression pertains to one dependent variable ($y$) and one independent variable ($x$): $y = f(x)$
Multiple regression (aka multivariable regression) pertains to one dependent variable and mult | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math
Simple regression pertains to one dependent variable ($y$) and one independent variable ($x$): $y = f(x)$
Multiple regression (aka multivariable regression) pertains to one dependent variable and multiple in... | Explain the difference between multiple regression and multivariate regression, with minimal use of
Simple regression pertains to one dependent variable ($y$) and one independent variable ($x$): $y = f(x)$
Multiple regression (aka multivariable regression) pertains to one dependent variable and mult |
2,251 | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math | I think the key insight (and differentiator) here aside from the number of variables on either side of the equation is that for the case of multivariate regression, the goal is to utilize the fact that there is (generally) correlation between response variables (or outcomes). For example, in a medical trial, predictors... | Explain the difference between multiple regression and multivariate regression, with minimal use of | I think the key insight (and differentiator) here aside from the number of variables on either side of the equation is that for the case of multivariate regression, the goal is to utilize the fact tha | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math
I think the key insight (and differentiator) here aside from the number of variables on either side of the equation is that for the case of multivariate regression, the goal is to utilize the fact that there... | Explain the difference between multiple regression and multivariate regression, with minimal use of
I think the key insight (and differentiator) here aside from the number of variables on either side of the equation is that for the case of multivariate regression, the goal is to utilize the fact tha |
2,252 | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math | In multivariate regression there are more than one dependent variable with different variances (or distributions). The predictor variables may be more than one or multiple. So it is may be a multiple regression with a matrix of dependent variables, i. e. multiple variances.
But when we say multiple regression, we mean... | Explain the difference between multiple regression and multivariate regression, with minimal use of | In multivariate regression there are more than one dependent variable with different variances (or distributions). The predictor variables may be more than one or multiple. So it is may be a multiple | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math
In multivariate regression there are more than one dependent variable with different variances (or distributions). The predictor variables may be more than one or multiple. So it is may be a multiple regress... | Explain the difference between multiple regression and multivariate regression, with minimal use of
In multivariate regression there are more than one dependent variable with different variances (or distributions). The predictor variables may be more than one or multiple. So it is may be a multiple |
2,253 | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math | There is no difference. This is because the maximum likelihood solution of the parameters of the joint problem $Y = W^T φ(x)$ with K target variables decouples to K independent regression problems, assuming a conditional distribution of the target vector to be an isotropic Gaussian of the form $p(t|φ(x),W, β) = N (t|W^... | Explain the difference between multiple regression and multivariate regression, with minimal use of | There is no difference. This is because the maximum likelihood solution of the parameters of the joint problem $Y = W^T φ(x)$ with K target variables decouples to K independent regression problems, as | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math
There is no difference. This is because the maximum likelihood solution of the parameters of the joint problem $Y = W^T φ(x)$ with K target variables decouples to K independent regression problems, assuming ... | Explain the difference between multiple regression and multivariate regression, with minimal use of
There is no difference. This is because the maximum likelihood solution of the parameters of the joint problem $Y = W^T φ(x)$ with K target variables decouples to K independent regression problems, as |
2,254 | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math | There ain’t no difference between multiple regression and multivariate regression in that, they both constitute a system with 2 or more independent variables and 1 or more dependent variables. As long as the outcome doesn’t depend on lag obs or a single predictor, it’s called multiple or multivariate regression otherwi... | Explain the difference between multiple regression and multivariate regression, with minimal use of | There ain’t no difference between multiple regression and multivariate regression in that, they both constitute a system with 2 or more independent variables and 1 or more dependent variables. As long | Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math
There ain’t no difference between multiple regression and multivariate regression in that, they both constitute a system with 2 or more independent variables and 1 or more dependent variables. As long as the... | Explain the difference between multiple regression and multivariate regression, with minimal use of
There ain’t no difference between multiple regression and multivariate regression in that, they both constitute a system with 2 or more independent variables and 1 or more dependent variables. As long |
2,255 | Understanding the role of the discount factor in reinforcement learning | TL;DR.
The fact that the discount rate is bounded to be smaller than 1 is a mathematical trick to make an infinite sum finite. This helps proving the convergence of certain algorithms.
In practice, the discount factor could be used to model the fact that the decision maker is uncertain about if in the next decision ins... | Understanding the role of the discount factor in reinforcement learning | TL;DR.
The fact that the discount rate is bounded to be smaller than 1 is a mathematical trick to make an infinite sum finite. This helps proving the convergence of certain algorithms.
In practice, th | Understanding the role of the discount factor in reinforcement learning
TL;DR.
The fact that the discount rate is bounded to be smaller than 1 is a mathematical trick to make an infinite sum finite. This helps proving the convergence of certain algorithms.
In practice, the discount factor could be used to model the fac... | Understanding the role of the discount factor in reinforcement learning
TL;DR.
The fact that the discount rate is bounded to be smaller than 1 is a mathematical trick to make an infinite sum finite. This helps proving the convergence of certain algorithms.
In practice, th |
2,256 | Understanding the role of the discount factor in reinforcement learning | TL;DR: Discount factors are associated with time horizons. Longer time horizons have have much more variance as they include more irrelevant information, while short time horizons are biased towards only short-term gains.
The discount factor essentially determines how much the reinforcement learning agents cares about ... | Understanding the role of the discount factor in reinforcement learning | TL;DR: Discount factors are associated with time horizons. Longer time horizons have have much more variance as they include more irrelevant information, while short time horizons are biased towards o | Understanding the role of the discount factor in reinforcement learning
TL;DR: Discount factors are associated with time horizons. Longer time horizons have have much more variance as they include more irrelevant information, while short time horizons are biased towards only short-term gains.
The discount factor essent... | Understanding the role of the discount factor in reinforcement learning
TL;DR: Discount factors are associated with time horizons. Longer time horizons have have much more variance as they include more irrelevant information, while short time horizons are biased towards o |
2,257 | Understanding the role of the discount factor in reinforcement learning | You're right that the discount factor (so-called $\gamma$ — note that this is different than $\lambda$ from TD-$\lambda$) acts like an "urgency of life" and is therefore part of the problem — just like it is in human lives: Some people live as if they'll live forever; some people live as if they're going to die tomorr... | Understanding the role of the discount factor in reinforcement learning | You're right that the discount factor (so-called $\gamma$ — note that this is different than $\lambda$ from TD-$\lambda$) acts like an "urgency of life" and is therefore part of the problem — just lik | Understanding the role of the discount factor in reinforcement learning
You're right that the discount factor (so-called $\gamma$ — note that this is different than $\lambda$ from TD-$\lambda$) acts like an "urgency of life" and is therefore part of the problem — just like it is in human lives: Some people live as if ... | Understanding the role of the discount factor in reinforcement learning
You're right that the discount factor (so-called $\gamma$ — note that this is different than $\lambda$ from TD-$\lambda$) acts like an "urgency of life" and is therefore part of the problem — just lik |
2,258 | Understanding the role of the discount factor in reinforcement learning | Inspired by "PolBM"'s answer, an intuitive example helps understand the usefulness of the discount factor. Imagining that there are two stocks we can purchase.
Stock A: Rising ten dollars on Monday of every week and falling ten dollars on Tuesday of every week.
Stock B: Falling ten dollars on Monday of every week and ... | Understanding the role of the discount factor in reinforcement learning | Inspired by "PolBM"'s answer, an intuitive example helps understand the usefulness of the discount factor. Imagining that there are two stocks we can purchase.
Stock A: Rising ten dollars on Monday o | Understanding the role of the discount factor in reinforcement learning
Inspired by "PolBM"'s answer, an intuitive example helps understand the usefulness of the discount factor. Imagining that there are two stocks we can purchase.
Stock A: Rising ten dollars on Monday of every week and falling ten dollars on Tuesday ... | Understanding the role of the discount factor in reinforcement learning
Inspired by "PolBM"'s answer, an intuitive example helps understand the usefulness of the discount factor. Imagining that there are two stocks we can purchase.
Stock A: Rising ten dollars on Monday o |
2,259 | Understanding the role of the discount factor in reinforcement learning | According to the paper:
Markov games as a framework for multi-agent reinforcement learning by Michael Littman, 1994, the notion of discount factor is defined in terms of the probability that the game will be allowed to continue. Even thought this paper is talking about Markov games, I believe the abstract can be used t... | Understanding the role of the discount factor in reinforcement learning | According to the paper:
Markov games as a framework for multi-agent reinforcement learning by Michael Littman, 1994, the notion of discount factor is defined in terms of the probability that the game | Understanding the role of the discount factor in reinforcement learning
According to the paper:
Markov games as a framework for multi-agent reinforcement learning by Michael Littman, 1994, the notion of discount factor is defined in terms of the probability that the game will be allowed to continue. Even thought this p... | Understanding the role of the discount factor in reinforcement learning
According to the paper:
Markov games as a framework for multi-agent reinforcement learning by Michael Littman, 1994, the notion of discount factor is defined in terms of the probability that the game |
2,260 | Can bootstrap be seen as a "cure" for the small sample size? | I remember reading that using the percentile confidence interval for bootstrapping is equivalent to using a Z interval instead of a T interval and using $n$ instead of $n-1$ for the denominator. Unfortunately I don't remember where I read this and could not find a reference in my quick searches. These differences don... | Can bootstrap be seen as a "cure" for the small sample size? | I remember reading that using the percentile confidence interval for bootstrapping is equivalent to using a Z interval instead of a T interval and using $n$ instead of $n-1$ for the denominator. Unfo | Can bootstrap be seen as a "cure" for the small sample size?
I remember reading that using the percentile confidence interval for bootstrapping is equivalent to using a Z interval instead of a T interval and using $n$ instead of $n-1$ for the denominator. Unfortunately I don't remember where I read this and could not ... | Can bootstrap be seen as a "cure" for the small sample size?
I remember reading that using the percentile confidence interval for bootstrapping is equivalent to using a Z interval instead of a T interval and using $n$ instead of $n-1$ for the denominator. Unfo |
2,261 | Can bootstrap be seen as a "cure" for the small sample size? | Other answers criticise the performance of bootstrap confidence intervals, not bootstrap itself. This is a different problem.
If your context satisfy the regularity conditions for the convergence of the bootstrap distribution (convergence in terms of the number of bootstrap samples), then the method will work if you us... | Can bootstrap be seen as a "cure" for the small sample size? | Other answers criticise the performance of bootstrap confidence intervals, not bootstrap itself. This is a different problem.
If your context satisfy the regularity conditions for the convergence of t | Can bootstrap be seen as a "cure" for the small sample size?
Other answers criticise the performance of bootstrap confidence intervals, not bootstrap itself. This is a different problem.
If your context satisfy the regularity conditions for the convergence of the bootstrap distribution (convergence in terms of the numb... | Can bootstrap be seen as a "cure" for the small sample size?
Other answers criticise the performance of bootstrap confidence intervals, not bootstrap itself. This is a different problem.
If your context satisfy the regularity conditions for the convergence of t |
2,262 | Can bootstrap be seen as a "cure" for the small sample size? | If you are provided with small sample size (as a sidelight, what is "small" seems to depend on some underlying customary rule in each research field), no bootstrap will do the magic. Assuming a database contains three observations for each of the two variables under investigation, no inference will make sense. In my ex... | Can bootstrap be seen as a "cure" for the small sample size? | If you are provided with small sample size (as a sidelight, what is "small" seems to depend on some underlying customary rule in each research field), no bootstrap will do the magic. Assuming a databa | Can bootstrap be seen as a "cure" for the small sample size?
If you are provided with small sample size (as a sidelight, what is "small" seems to depend on some underlying customary rule in each research field), no bootstrap will do the magic. Assuming a database contains three observations for each of the two variable... | Can bootstrap be seen as a "cure" for the small sample size?
If you are provided with small sample size (as a sidelight, what is "small" seems to depend on some underlying customary rule in each research field), no bootstrap will do the magic. Assuming a databa |
2,263 | Can bootstrap be seen as a "cure" for the small sample size? | Bootstrap works well in small sample sizes by ensuring the correctness of tests (e.g. that the nominal 0.05 significance level is close to the actual size of the test), however the bootstrap does not magically grant you extra power. If you have a small sample, you have little power, end of story.
Parametric (linear mo... | Can bootstrap be seen as a "cure" for the small sample size? | Bootstrap works well in small sample sizes by ensuring the correctness of tests (e.g. that the nominal 0.05 significance level is close to the actual size of the test), however the bootstrap does not | Can bootstrap be seen as a "cure" for the small sample size?
Bootstrap works well in small sample sizes by ensuring the correctness of tests (e.g. that the nominal 0.05 significance level is close to the actual size of the test), however the bootstrap does not magically grant you extra power. If you have a small sample... | Can bootstrap be seen as a "cure" for the small sample size?
Bootstrap works well in small sample sizes by ensuring the correctness of tests (e.g. that the nominal 0.05 significance level is close to the actual size of the test), however the bootstrap does not |
2,264 | What is the single most influential book every statistician should read? [closed] | Here are two to put on the list:
Tufte. The visual display of quantitative information
Tukey. Exploratory data analysis | What is the single most influential book every statistician should read? [closed] | Here are two to put on the list:
Tufte. The visual display of quantitative information
Tukey. Exploratory data analysis | What is the single most influential book every statistician should read? [closed]
Here are two to put on the list:
Tufte. The visual display of quantitative information
Tukey. Exploratory data analysis | What is the single most influential book every statistician should read? [closed]
Here are two to put on the list:
Tufte. The visual display of quantitative information
Tukey. Exploratory data analysis |
2,265 | What is the single most influential book every statistician should read? [closed] | The Elements of Statistical Learning from Hastie, Tibshirani and Friedman http://www-stat.stanford.edu/~tibs/ElemStatLearn/ should be in any statistician's library ! | What is the single most influential book every statistician should read? [closed] | The Elements of Statistical Learning from Hastie, Tibshirani and Friedman http://www-stat.stanford.edu/~tibs/ElemStatLearn/ should be in any statistician's library ! | What is the single most influential book every statistician should read? [closed]
The Elements of Statistical Learning from Hastie, Tibshirani and Friedman http://www-stat.stanford.edu/~tibs/ElemStatLearn/ should be in any statistician's library ! | What is the single most influential book every statistician should read? [closed]
The Elements of Statistical Learning from Hastie, Tibshirani and Friedman http://www-stat.stanford.edu/~tibs/ElemStatLearn/ should be in any statistician's library ! |
2,266 | What is the single most influential book every statistician should read? [closed] | I am no statistician, and I haven't read that much on the topic, but perhaps
Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century
should be mentioned? It is no textbook, but still worth reading. | What is the single most influential book every statistician should read? [closed] | I am no statistician, and I haven't read that much on the topic, but perhaps
Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century
should be mentioned? It is no textbook, b | What is the single most influential book every statistician should read? [closed]
I am no statistician, and I haven't read that much on the topic, but perhaps
Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century
should be mentioned? It is no textbook, but still worth reading. | What is the single most influential book every statistician should read? [closed]
I am no statistician, and I haven't read that much on the topic, but perhaps
Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century
should be mentioned? It is no textbook, b |
2,267 | What is the single most influential book every statistician should read? [closed] | Probability Theory: The Logic of Science | What is the single most influential book every statistician should read? [closed] | Probability Theory: The Logic of Science | What is the single most influential book every statistician should read? [closed]
Probability Theory: The Logic of Science | What is the single most influential book every statistician should read? [closed]
Probability Theory: The Logic of Science |
2,268 | What is the single most influential book every statistician should read? [closed] | Darrell Huff -- How to Lie with Statistics | What is the single most influential book every statistician should read? [closed] | Darrell Huff -- How to Lie with Statistics | What is the single most influential book every statistician should read? [closed]
Darrell Huff -- How to Lie with Statistics | What is the single most influential book every statistician should read? [closed]
Darrell Huff -- How to Lie with Statistics |
2,269 | What is the single most influential book every statistician should read? [closed] | Not a book, but I recently discovered an article by Jacob Cohen in American Psychologist entitled "Things I have learned (so far)." It's available as a pdf here. | What is the single most influential book every statistician should read? [closed] | Not a book, but I recently discovered an article by Jacob Cohen in American Psychologist entitled "Things I have learned (so far)." It's available as a pdf here. | What is the single most influential book every statistician should read? [closed]
Not a book, but I recently discovered an article by Jacob Cohen in American Psychologist entitled "Things I have learned (so far)." It's available as a pdf here. | What is the single most influential book every statistician should read? [closed]
Not a book, but I recently discovered an article by Jacob Cohen in American Psychologist entitled "Things I have learned (so far)." It's available as a pdf here. |
2,270 | What is the single most influential book every statistician should read? [closed] | Long ago, Jack Kiefer's little monograph "Introduction to Statistical Inference" peeled away the mystery of a great deal of classical statistics and helped me get started with the rest of the literature. I still refer to it and warmly recommend it to strong students in second-year stats courses. | What is the single most influential book every statistician should read? [closed] | Long ago, Jack Kiefer's little monograph "Introduction to Statistical Inference" peeled away the mystery of a great deal of classical statistics and helped me get started with the rest of the literatu | What is the single most influential book every statistician should read? [closed]
Long ago, Jack Kiefer's little monograph "Introduction to Statistical Inference" peeled away the mystery of a great deal of classical statistics and helped me get started with the rest of the literature. I still refer to it and warmly re... | What is the single most influential book every statistician should read? [closed]
Long ago, Jack Kiefer's little monograph "Introduction to Statistical Inference" peeled away the mystery of a great deal of classical statistics and helped me get started with the rest of the literatu |
2,271 | What is the single most influential book every statistician should read? [closed] | I wouldn't argue that either of these should be considered "the most influential book... [for] statistician[s]", but for those who are just starting to learn about the topic, two helpful books are:
Robert Abelson, Statistics as Principled Argument
Paul Murrell, Introduction to Data Technologies | What is the single most influential book every statistician should read? [closed] | I wouldn't argue that either of these should be considered "the most influential book... [for] statistician[s]", but for those who are just starting to learn about the topic, two helpful books are:
R | What is the single most influential book every statistician should read? [closed]
I wouldn't argue that either of these should be considered "the most influential book... [for] statistician[s]", but for those who are just starting to learn about the topic, two helpful books are:
Robert Abelson, Statistics as Principle... | What is the single most influential book every statistician should read? [closed]
I wouldn't argue that either of these should be considered "the most influential book... [for] statistician[s]", but for those who are just starting to learn about the topic, two helpful books are:
R |
2,272 | What is the single most influential book every statistician should read? [closed] | I think every statistician should read Stigler's The History of Statistics: The Measurement of Uncertainty before 1900
It is beautifully written, thorough and it isn't a historian's perspective but a mathematician's, hence it doesn't avoid the technical details. | What is the single most influential book every statistician should read? [closed] | I think every statistician should read Stigler's The History of Statistics: The Measurement of Uncertainty before 1900
It is beautifully written, thorough and it isn't a historian's perspective but a | What is the single most influential book every statistician should read? [closed]
I think every statistician should read Stigler's The History of Statistics: The Measurement of Uncertainty before 1900
It is beautifully written, thorough and it isn't a historian's perspective but a mathematician's, hence it doesn't avoi... | What is the single most influential book every statistician should read? [closed]
I think every statistician should read Stigler's The History of Statistics: The Measurement of Uncertainty before 1900
It is beautifully written, thorough and it isn't a historian's perspective but a |
2,273 | What is the single most influential book every statistician should read? [closed] | William Cleveland's book "The Elements of Graphing Data" or his book "Visualizing Data" | What is the single most influential book every statistician should read? [closed] | William Cleveland's book "The Elements of Graphing Data" or his book "Visualizing Data" | What is the single most influential book every statistician should read? [closed]
William Cleveland's book "The Elements of Graphing Data" or his book "Visualizing Data" | What is the single most influential book every statistician should read? [closed]
William Cleveland's book "The Elements of Graphing Data" or his book "Visualizing Data" |
2,274 | What is the single most influential book every statistician should read? [closed] | I say the visual display of quantitative information by Tufte, and Freakonomics for something fun. | What is the single most influential book every statistician should read? [closed] | I say the visual display of quantitative information by Tufte, and Freakonomics for something fun. | What is the single most influential book every statistician should read? [closed]
I say the visual display of quantitative information by Tufte, and Freakonomics for something fun. | What is the single most influential book every statistician should read? [closed]
I say the visual display of quantitative information by Tufte, and Freakonomics for something fun. |
2,275 | What is the single most influential book every statistician should read? [closed] | Andrew Gelman's interesting book recommendations are here:
http://thebrowser.com/interviews/andrew-gelman-on-statistics | What is the single most influential book every statistician should read? [closed] | Andrew Gelman's interesting book recommendations are here:
http://thebrowser.com/interviews/andrew-gelman-on-statistics | What is the single most influential book every statistician should read? [closed]
Andrew Gelman's interesting book recommendations are here:
http://thebrowser.com/interviews/andrew-gelman-on-statistics | What is the single most influential book every statistician should read? [closed]
Andrew Gelman's interesting book recommendations are here:
http://thebrowser.com/interviews/andrew-gelman-on-statistics |
2,276 | What is the single most influential book every statistician should read? [closed] | In addition to "The History of Statistics" suggested by Graham, another Stigler book worth reading is
Statistics on the Table: The History of Statistical Concepts and Methods | What is the single most influential book every statistician should read? [closed] | In addition to "The History of Statistics" suggested by Graham, another Stigler book worth reading is
Statistics on the Table: The History of Statistical Concepts and Methods | What is the single most influential book every statistician should read? [closed]
In addition to "The History of Statistics" suggested by Graham, another Stigler book worth reading is
Statistics on the Table: The History of Statistical Concepts and Methods | What is the single most influential book every statistician should read? [closed]
In addition to "The History of Statistics" suggested by Graham, another Stigler book worth reading is
Statistics on the Table: The History of Statistical Concepts and Methods |
2,277 | What is the single most influential book every statistician should read? [closed] | On the math/foundations side: Harald Cramér's Mathematical Methods of Statistics. | What is the single most influential book every statistician should read? [closed] | On the math/foundations side: Harald Cramér's Mathematical Methods of Statistics. | What is the single most influential book every statistician should read? [closed]
On the math/foundations side: Harald Cramér's Mathematical Methods of Statistics. | What is the single most influential book every statistician should read? [closed]
On the math/foundations side: Harald Cramér's Mathematical Methods of Statistics. |
2,278 | What is the single most influential book every statistician should read? [closed] | For a clear exposition of what should be in social science journal articles (assistance if you're writing or peer reviewing) I like The Reviewer's Guide to Quantitative Methods in the Social Sciences. In particular I like the table desideratra as a synopsis of the minimum that a paper (article, thesis, dissertation) sh... | What is the single most influential book every statistician should read? [closed] | For a clear exposition of what should be in social science journal articles (assistance if you're writing or peer reviewing) I like The Reviewer's Guide to Quantitative Methods in the Social Sciences. | What is the single most influential book every statistician should read? [closed]
For a clear exposition of what should be in social science journal articles (assistance if you're writing or peer reviewing) I like The Reviewer's Guide to Quantitative Methods in the Social Sciences. In particular I like the table deside... | What is the single most influential book every statistician should read? [closed]
For a clear exposition of what should be in social science journal articles (assistance if you're writing or peer reviewing) I like The Reviewer's Guide to Quantitative Methods in the Social Sciences. |
2,279 | What is the single most influential book every statistician should read? [closed] | Fooled By Randomness by Taleb
Taleb is a professor at Columbia and an options trader. He made about $800 million dollars in 2008 betting against the market. He also wrote Black Swan. He discusses the absurdity of using the normal distribution to model markets, and philosophizes on our ability to use induction. | What is the single most influential book every statistician should read? [closed] | Fooled By Randomness by Taleb
Taleb is a professor at Columbia and an options trader. He made about $800 million dollars in 2008 betting against the market. He also wrote Black Swan. He discusses the | What is the single most influential book every statistician should read? [closed]
Fooled By Randomness by Taleb
Taleb is a professor at Columbia and an options trader. He made about $800 million dollars in 2008 betting against the market. He also wrote Black Swan. He discusses the absurdity of using the normal distribu... | What is the single most influential book every statistician should read? [closed]
Fooled By Randomness by Taleb
Taleb is a professor at Columbia and an options trader. He made about $800 million dollars in 2008 betting against the market. He also wrote Black Swan. He discusses the |
2,280 | What is the single most influential book every statistician should read? [closed] | I have read the above recommendations and was surprised to find that most of the people who answered the question were people who are not statisticians themselves. With 2 or 3 exceptions ...
As an industrial statistician who also happened to work with social scientists and health professionals I would say that if I co... | What is the single most influential book every statistician should read? [closed] | I have read the above recommendations and was surprised to find that most of the people who answered the question were people who are not statisticians themselves. With 2 or 3 exceptions ...
As an in | What is the single most influential book every statistician should read? [closed]
I have read the above recommendations and was surprised to find that most of the people who answered the question were people who are not statisticians themselves. With 2 or 3 exceptions ...
As an industrial statistician who also happene... | What is the single most influential book every statistician should read? [closed]
I have read the above recommendations and was surprised to find that most of the people who answered the question were people who are not statisticians themselves. With 2 or 3 exceptions ...
As an in |
2,281 | What is the single most influential book every statistician should read? [closed] | Michael Oakes' Statistical Inference: A Commentary for the Social and Behavioral Sciences.
Elazar Pedhazur's Multiple Regression in Behavioral Research. If you can stand the immense detail and the self-important tone.
In case you're interested, I've reviewed both on Amazon and at https://yellowbrickstats.com/favorit... | What is the single most influential book every statistician should read? [closed] | Michael Oakes' Statistical Inference: A Commentary for the Social and Behavioral Sciences.
Elazar Pedhazur's Multiple Regression in Behavioral Research. If you can stand the immense detail and the s | What is the single most influential book every statistician should read? [closed]
Michael Oakes' Statistical Inference: A Commentary for the Social and Behavioral Sciences.
Elazar Pedhazur's Multiple Regression in Behavioral Research. If you can stand the immense detail and the self-important tone.
In case you're in... | What is the single most influential book every statistician should read? [closed]
Michael Oakes' Statistical Inference: A Commentary for the Social and Behavioral Sciences.
Elazar Pedhazur's Multiple Regression in Behavioral Research. If you can stand the immense detail and the s |
2,282 | What is the single most influential book every statistician should read? [closed] | Rice: Mathematical Statistics and Data Analysis | What is the single most influential book every statistician should read? [closed] | Rice: Mathematical Statistics and Data Analysis | What is the single most influential book every statistician should read? [closed]
Rice: Mathematical Statistics and Data Analysis | What is the single most influential book every statistician should read? [closed]
Rice: Mathematical Statistics and Data Analysis |
2,283 | What is the single most influential book every statistician should read? [closed] | Lots of good books already suggested. But here is another: Gerd Gigerenzer's "Reckoning With Risk" because understanding how statistics affect decisions is more important than getting all the theory right. In fact number one sin of statisticians is failing to communicate clearly. His book talks about the consequences o... | What is the single most influential book every statistician should read? [closed] | Lots of good books already suggested. But here is another: Gerd Gigerenzer's "Reckoning With Risk" because understanding how statistics affect decisions is more important than getting all the theory r | What is the single most influential book every statistician should read? [closed]
Lots of good books already suggested. But here is another: Gerd Gigerenzer's "Reckoning With Risk" because understanding how statistics affect decisions is more important than getting all the theory right. In fact number one sin of statis... | What is the single most influential book every statistician should read? [closed]
Lots of good books already suggested. But here is another: Gerd Gigerenzer's "Reckoning With Risk" because understanding how statistics affect decisions is more important than getting all the theory r |
2,284 | What is the single most influential book every statistician should read? [closed] | I learned a great deal from the Bible of Bayesian statistics:
Jose Bernardo and Adrian Smith (2000) Bayesian Theory. | What is the single most influential book every statistician should read? [closed] | I learned a great deal from the Bible of Bayesian statistics:
Jose Bernardo and Adrian Smith (2000) Bayesian Theory. | What is the single most influential book every statistician should read? [closed]
I learned a great deal from the Bible of Bayesian statistics:
Jose Bernardo and Adrian Smith (2000) Bayesian Theory. | What is the single most influential book every statistician should read? [closed]
I learned a great deal from the Bible of Bayesian statistics:
Jose Bernardo and Adrian Smith (2000) Bayesian Theory. |
2,285 | What is the single most influential book every statistician should read? [closed] | It would probably be Bayesian Data Analysis by Gelman or Deep Learning with Python. But that's a bit like taking streptomycin to the middle ages. These were not written when I started my career and quite a few things from the books would have been big news back then. Some of the most influential things everyone should ... | What is the single most influential book every statistician should read? [closed] | It would probably be Bayesian Data Analysis by Gelman or Deep Learning with Python. But that's a bit like taking streptomycin to the middle ages. These were not written when I started my career and qu | What is the single most influential book every statistician should read? [closed]
It would probably be Bayesian Data Analysis by Gelman or Deep Learning with Python. But that's a bit like taking streptomycin to the middle ages. These were not written when I started my career and quite a few things from the books would ... | What is the single most influential book every statistician should read? [closed]
It would probably be Bayesian Data Analysis by Gelman or Deep Learning with Python. But that's a bit like taking streptomycin to the middle ages. These were not written when I started my career and qu |
2,286 | What is the single most influential book every statistician should read? [closed] | I am going to go ahead and propose a standard textbook in the field. I am talking about Probability and Statistics by DeGroot and Schervish, first published in 1975.
This book has served as a textbook for many students and is considered a classic, rightfully so in my opinion. It covers topics such as combinatorics, dis... | What is the single most influential book every statistician should read? [closed] | I am going to go ahead and propose a standard textbook in the field. I am talking about Probability and Statistics by DeGroot and Schervish, first published in 1975.
This book has served as a textbook | What is the single most influential book every statistician should read? [closed]
I am going to go ahead and propose a standard textbook in the field. I am talking about Probability and Statistics by DeGroot and Schervish, first published in 1975.
This book has served as a textbook for many students and is considered a... | What is the single most influential book every statistician should read? [closed]
I am going to go ahead and propose a standard textbook in the field. I am talking about Probability and Statistics by DeGroot and Schervish, first published in 1975.
This book has served as a textbook |
2,287 | What is the single most influential book every statistician should read? [closed] | The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results by Paul D. Ellis
This book if a "must have" for everyone conducting any scientific research, especially one that comes not from pure stats/maths. The book below extends the first one regarding confidence in... | What is the single most influential book every statistician should read? [closed] | The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results by Paul D. Ellis
This book if a "must have" for everyone conducting any scientific res | What is the single most influential book every statistician should read? [closed]
The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results by Paul D. Ellis
This book if a "must have" for everyone conducting any scientific research, especially one that comes not f... | What is the single most influential book every statistician should read? [closed]
The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results by Paul D. Ellis
This book if a "must have" for everyone conducting any scientific res |
2,288 | What is the single most influential book every statistician should read? [closed] | Kennedy's A Guide to Econometrics contains a wealth of practical advice about a wide range of statistical analysis. It's somehow both incredibly information-dense and easy to read, and I still learn something new every time I pick it up.
Wooldridge's Introductory Econometrics has a good amount of this kind of discussi... | What is the single most influential book every statistician should read? [closed] | Kennedy's A Guide to Econometrics contains a wealth of practical advice about a wide range of statistical analysis. It's somehow both incredibly information-dense and easy to read, and I still learn | What is the single most influential book every statistician should read? [closed]
Kennedy's A Guide to Econometrics contains a wealth of practical advice about a wide range of statistical analysis. It's somehow both incredibly information-dense and easy to read, and I still learn something new every time I pick it up.... | What is the single most influential book every statistician should read? [closed]
Kennedy's A Guide to Econometrics contains a wealth of practical advice about a wide range of statistical analysis. It's somehow both incredibly information-dense and easy to read, and I still learn |
2,289 | What is the single most influential book every statistician should read? [closed] | "Most influential" is a very different notion from "everyone should read". I am not qualified to answer the first - you'd need someone who is an historian of statistics - but for the second, here are some:
Statistics as Principled Argument by Robert Abelson should be read by anyone doing or using statistics in the pur... | What is the single most influential book every statistician should read? [closed] | "Most influential" is a very different notion from "everyone should read". I am not qualified to answer the first - you'd need someone who is an historian of statistics - but for the second, here are | What is the single most influential book every statistician should read? [closed]
"Most influential" is a very different notion from "everyone should read". I am not qualified to answer the first - you'd need someone who is an historian of statistics - but for the second, here are some:
Statistics as Principled Argume... | What is the single most influential book every statistician should read? [closed]
"Most influential" is a very different notion from "everyone should read". I am not qualified to answer the first - you'd need someone who is an historian of statistics - but for the second, here are |
2,290 | What is the meaning of "All models are wrong, but some are useful" | I think its meaning is best analyzed by looking at it in two parts:
"All models are wrong" that is, every model is wrong because it is a simplification of reality. Some models, especially in the "hard" sciences, are only a little wrong. They ignore things like friction or the gravitational effect of tiny bodies. Other... | What is the meaning of "All models are wrong, but some are useful" | I think its meaning is best analyzed by looking at it in two parts:
"All models are wrong" that is, every model is wrong because it is a simplification of reality. Some models, especially in the "har | What is the meaning of "All models are wrong, but some are useful"
I think its meaning is best analyzed by looking at it in two parts:
"All models are wrong" that is, every model is wrong because it is a simplification of reality. Some models, especially in the "hard" sciences, are only a little wrong. They ignore thi... | What is the meaning of "All models are wrong, but some are useful"
I think its meaning is best analyzed by looking at it in two parts:
"All models are wrong" that is, every model is wrong because it is a simplification of reality. Some models, especially in the "har |
2,291 | What is the meaning of "All models are wrong, but some are useful" | It means useful insights can be provided from models which are not a perfect representation of the phenomena they model.
A statistical model is a description of a system using mathematical concepts. As such in many cases you add a certain layer of abstraction to facilitate your inferential procedure (eg. normality of m... | What is the meaning of "All models are wrong, but some are useful" | It means useful insights can be provided from models which are not a perfect representation of the phenomena they model.
A statistical model is a description of a system using mathematical concepts. A | What is the meaning of "All models are wrong, but some are useful"
It means useful insights can be provided from models which are not a perfect representation of the phenomena they model.
A statistical model is a description of a system using mathematical concepts. As such in many cases you add a certain layer of abstr... | What is the meaning of "All models are wrong, but some are useful"
It means useful insights can be provided from models which are not a perfect representation of the phenomena they model.
A statistical model is a description of a system using mathematical concepts. A |
2,292 | What is the meaning of "All models are wrong, but some are useful" | I found this 2009 JSA talk by Thad Tarpey to provide a useful explanation and commentary on the Box passage. He argues that if we regard models as approximations to the truth, we could just as easily call all models right.
Here’s the abstract:
Students of statistics are often introduced to George Box’s famous
quote:... | What is the meaning of "All models are wrong, but some are useful" | I found this 2009 JSA talk by Thad Tarpey to provide a useful explanation and commentary on the Box passage. He argues that if we regard models as approximations to the truth, we could just as easily | What is the meaning of "All models are wrong, but some are useful"
I found this 2009 JSA talk by Thad Tarpey to provide a useful explanation and commentary on the Box passage. He argues that if we regard models as approximations to the truth, we could just as easily call all models right.
Here’s the abstract:
Students... | What is the meaning of "All models are wrong, but some are useful"
I found this 2009 JSA talk by Thad Tarpey to provide a useful explanation and commentary on the Box passage. He argues that if we regard models as approximations to the truth, we could just as easily |
2,293 | What is the meaning of "All models are wrong, but some are useful" | For me the actual insight lies in the following aspect:
A model doesn't have to be correct to be useful.
Unfortunately in many sciences it is often forgotten that models don't necessarily need to be exact representations of reality to allow new discoveries and predictions!
So don't waste your time building a complica... | What is the meaning of "All models are wrong, but some are useful" | For me the actual insight lies in the following aspect:
A model doesn't have to be correct to be useful.
Unfortunately in many sciences it is often forgotten that models don't necessarily need to be | What is the meaning of "All models are wrong, but some are useful"
For me the actual insight lies in the following aspect:
A model doesn't have to be correct to be useful.
Unfortunately in many sciences it is often forgotten that models don't necessarily need to be exact representations of reality to allow new discov... | What is the meaning of "All models are wrong, but some are useful"
For me the actual insight lies in the following aspect:
A model doesn't have to be correct to be useful.
Unfortunately in many sciences it is often forgotten that models don't necessarily need to be |
2,294 | What is the meaning of "All models are wrong, but some are useful" | Because no one has added it, George Box used the phase quoted to introduce the following section in a book. I believe he does the best job of explaining what he meant:
Now it would be very remarkable if any system existing in the real world could be exactly represented by any simple model. However, cunningly chosen p... | What is the meaning of "All models are wrong, but some are useful" | Because no one has added it, George Box used the phase quoted to introduce the following section in a book. I believe he does the best job of explaining what he meant:
Now it would be very remarkabl | What is the meaning of "All models are wrong, but some are useful"
Because no one has added it, George Box used the phase quoted to introduce the following section in a book. I believe he does the best job of explaining what he meant:
Now it would be very remarkable if any system existing in the real world could be e... | What is the meaning of "All models are wrong, but some are useful"
Because no one has added it, George Box used the phase quoted to introduce the following section in a book. I believe he does the best job of explaining what he meant:
Now it would be very remarkabl |
2,295 | What is the meaning of "All models are wrong, but some are useful" | If I may, than just one more comment may be useful. The version of the prase that I prefer is
(...) all models are approximations. Essentially, all models are wrong, but
some are useful (...)
taken from Response Surfaces, Mixtures, and Ridge Analyses by Box and Draper (2007, p. 414, Wiley). Looking at the extended ... | What is the meaning of "All models are wrong, but some are useful" | If I may, than just one more comment may be useful. The version of the prase that I prefer is
(...) all models are approximations. Essentially, all models are wrong, but
some are useful (...)
take | What is the meaning of "All models are wrong, but some are useful"
If I may, than just one more comment may be useful. The version of the prase that I prefer is
(...) all models are approximations. Essentially, all models are wrong, but
some are useful (...)
taken from Response Surfaces, Mixtures, and Ridge Analyse... | What is the meaning of "All models are wrong, but some are useful"
If I may, than just one more comment may be useful. The version of the prase that I prefer is
(...) all models are approximations. Essentially, all models are wrong, but
some are useful (...)
take |
2,296 | What is the meaning of "All models are wrong, but some are useful" | You might think of it this way. the maximum complexity (i.e., entropy) of an object obeys some form of the Bekenstein bound:
$$
I \le \frac{2\pi RE}{\hbar c\ln 2}
$$
where $E$ is the total rest energy including mass, and $R$ is the radius of a sphere that encloses the object.
That's a big number, in most cases:
The B... | What is the meaning of "All models are wrong, but some are useful" | You might think of it this way. the maximum complexity (i.e., entropy) of an object obeys some form of the Bekenstein bound:
$$
I \le \frac{2\pi RE}{\hbar c\ln 2}
$$
where $E$ is the total rest energ | What is the meaning of "All models are wrong, but some are useful"
You might think of it this way. the maximum complexity (i.e., entropy) of an object obeys some form of the Bekenstein bound:
$$
I \le \frac{2\pi RE}{\hbar c\ln 2}
$$
where $E$ is the total rest energy including mass, and $R$ is the radius of a sphere t... | What is the meaning of "All models are wrong, but some are useful"
You might think of it this way. the maximum complexity (i.e., entropy) of an object obeys some form of the Bekenstein bound:
$$
I \le \frac{2\pi RE}{\hbar c\ln 2}
$$
where $E$ is the total rest energ |
2,297 | What is the meaning of "All models are wrong, but some are useful" | A model cannot provide 100% accurate predictions if there is any randomness in the outcomes. If there was no uncertainty, no randomness, and no error, then it would be considered a fact rather than a model.
The first is very important, because models are frequently used for modelling expectations of events that have no... | What is the meaning of "All models are wrong, but some are useful" | A model cannot provide 100% accurate predictions if there is any randomness in the outcomes. If there was no uncertainty, no randomness, and no error, then it would be considered a fact rather than a | What is the meaning of "All models are wrong, but some are useful"
A model cannot provide 100% accurate predictions if there is any randomness in the outcomes. If there was no uncertainty, no randomness, and no error, then it would be considered a fact rather than a model.
The first is very important, because models ar... | What is the meaning of "All models are wrong, but some are useful"
A model cannot provide 100% accurate predictions if there is any randomness in the outcomes. If there was no uncertainty, no randomness, and no error, then it would be considered a fact rather than a |
2,298 | What is the meaning of "All models are wrong, but some are useful" | I think Peter and user11852 gave great answers. I would also add (by negation) that if a model was really good, it would probably be useless because of overfitting (hence, not generalizable). | What is the meaning of "All models are wrong, but some are useful" | I think Peter and user11852 gave great answers. I would also add (by negation) that if a model was really good, it would probably be useless because of overfitting (hence, not generalizable). | What is the meaning of "All models are wrong, but some are useful"
I think Peter and user11852 gave great answers. I would also add (by negation) that if a model was really good, it would probably be useless because of overfitting (hence, not generalizable). | What is the meaning of "All models are wrong, but some are useful"
I think Peter and user11852 gave great answers. I would also add (by negation) that if a model was really good, it would probably be useless because of overfitting (hence, not generalizable). |
2,299 | What is the meaning of "All models are wrong, but some are useful" | My acid interpretation is: Believing that a mathematical model describes exactly all the factors, and their interactions, governing a phenomenon of interest would be too simplistic and arrogant. We do not even know if the logic we use is enough to understand our universe. However, some mathematical models represent a g... | What is the meaning of "All models are wrong, but some are useful" | My acid interpretation is: Believing that a mathematical model describes exactly all the factors, and their interactions, governing a phenomenon of interest would be too simplistic and arrogant. We do | What is the meaning of "All models are wrong, but some are useful"
My acid interpretation is: Believing that a mathematical model describes exactly all the factors, and their interactions, governing a phenomenon of interest would be too simplistic and arrogant. We do not even know if the logic we use is enough to under... | What is the meaning of "All models are wrong, but some are useful"
My acid interpretation is: Believing that a mathematical model describes exactly all the factors, and their interactions, governing a phenomenon of interest would be too simplistic and arrogant. We do |
2,300 | What is the meaning of "All models are wrong, but some are useful" | As an astrostatistician (a rare breed perhaps), I find the fame of Box's dictum to be unfortunate. In the physical sciences, we often have a strong consensus for understanding the processes underlying of an observed phenomenon, and these processes can often expressed by mathematical models arising from the laws of gra... | What is the meaning of "All models are wrong, but some are useful" | As an astrostatistician (a rare breed perhaps), I find the fame of Box's dictum to be unfortunate. In the physical sciences, we often have a strong consensus for understanding the processes underlyin | What is the meaning of "All models are wrong, but some are useful"
As an astrostatistician (a rare breed perhaps), I find the fame of Box's dictum to be unfortunate. In the physical sciences, we often have a strong consensus for understanding the processes underlying of an observed phenomenon, and these processes can ... | What is the meaning of "All models are wrong, but some are useful"
As an astrostatistician (a rare breed perhaps), I find the fame of Box's dictum to be unfortunate. In the physical sciences, we often have a strong consensus for understanding the processes underlyin |
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