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 |
|---|---|---|---|---|---|---|
15,301 | Can someone please explain dynamic time warping for determining time series similarity? | Dynamic time warping makes a particular assumption on your data set: one vector is a non-linear time-streteched series of the other. But it also assumes that the actual values are on the same scale.
Lets say you have: $x=1..10000$, $a(x)=1\cdot\sin(0.01*x)$, $b(x)=1\cdot\sin(0.01234*x)$,$c(x)=1000\cdot\sin(0.01*x)$.
Th... | Can someone please explain dynamic time warping for determining time series similarity? | Dynamic time warping makes a particular assumption on your data set: one vector is a non-linear time-streteched series of the other. But it also assumes that the actual values are on the same scale.
L | Can someone please explain dynamic time warping for determining time series similarity?
Dynamic time warping makes a particular assumption on your data set: one vector is a non-linear time-streteched series of the other. But it also assumes that the actual values are on the same scale.
Lets say you have: $x=1..10000$, ... | Can someone please explain dynamic time warping for determining time series similarity?
Dynamic time warping makes a particular assumption on your data set: one vector is a non-linear time-streteched series of the other. But it also assumes that the actual values are on the same scale.
L |
15,302 | Can someone please explain dynamic time warping for determining time series similarity? | First, you say "dynamic time warping metric", however DTW is a distance measure, but not a metric (it does not obey the triangular inequality).
Paper [a] compares DTW to 12 alternatives on 43 datasets, DTW really does work very well for most problems.
If you want to learn more about DTW, you could glance at Keoghs tuto... | Can someone please explain dynamic time warping for determining time series similarity? | First, you say "dynamic time warping metric", however DTW is a distance measure, but not a metric (it does not obey the triangular inequality).
Paper [a] compares DTW to 12 alternatives on 43 datasets | Can someone please explain dynamic time warping for determining time series similarity?
First, you say "dynamic time warping metric", however DTW is a distance measure, but not a metric (it does not obey the triangular inequality).
Paper [a] compares DTW to 12 alternatives on 43 datasets, DTW really does work very well... | Can someone please explain dynamic time warping for determining time series similarity?
First, you say "dynamic time warping metric", however DTW is a distance measure, but not a metric (it does not obey the triangular inequality).
Paper [a] compares DTW to 12 alternatives on 43 datasets |
15,303 | Can someone please explain dynamic time warping for determining time series similarity? | In the 1980s dynamic time warping was the method used for template matching in speech recognition. The aim was to try to match time series of analyzed speech to stored templates, usually of whole words. The difficulty is people speak at different rates. DTW was used to register the unknown pattern to the template. It w... | Can someone please explain dynamic time warping for determining time series similarity? | In the 1980s dynamic time warping was the method used for template matching in speech recognition. The aim was to try to match time series of analyzed speech to stored templates, usually of whole word | Can someone please explain dynamic time warping for determining time series similarity?
In the 1980s dynamic time warping was the method used for template matching in speech recognition. The aim was to try to match time series of analyzed speech to stored templates, usually of whole words. The difficulty is people spea... | Can someone please explain dynamic time warping for determining time series similarity?
In the 1980s dynamic time warping was the method used for template matching in speech recognition. The aim was to try to match time series of analyzed speech to stored templates, usually of whole word |
15,304 | Why does chi-square testing use the expected count as the variance? | The general form for many test statistics is
$\frac{observed - expected}{standard error}$
In the case of a normal variable the standard error is based on either the known population variance (z-stats) or the estimate from the sample (t-stats). With the binomial the standard error is based on the proportion (hypothesi... | Why does chi-square testing use the expected count as the variance? | The general form for many test statistics is
$\frac{observed - expected}{standard error}$
In the case of a normal variable the standard error is based on either the known population variance (z-stats | Why does chi-square testing use the expected count as the variance?
The general form for many test statistics is
$\frac{observed - expected}{standard error}$
In the case of a normal variable the standard error is based on either the known population variance (z-stats) or the estimate from the sample (t-stats). With t... | Why does chi-square testing use the expected count as the variance?
The general form for many test statistics is
$\frac{observed - expected}{standard error}$
In the case of a normal variable the standard error is based on either the known population variance (z-stats |
15,305 | Why does chi-square testing use the expected count as the variance? | Let's handle the simplest case to try to provide the most
intuition. Let $X_1, X_2, \ldots, X_n$ be an iid sample from a discrete
distribution with $k$ outcomes. Let $\pi_1,\ldots,\pi_k$ be the
probabilities of each particular outcome. We are interested in the
(asymptotic) distribution of the chi-squared statistic
$$
... | Why does chi-square testing use the expected count as the variance? | Let's handle the simplest case to try to provide the most
intuition. Let $X_1, X_2, \ldots, X_n$ be an iid sample from a discrete
distribution with $k$ outcomes. Let $\pi_1,\ldots,\pi_k$ be the
probab | Why does chi-square testing use the expected count as the variance?
Let's handle the simplest case to try to provide the most
intuition. Let $X_1, X_2, \ldots, X_n$ be an iid sample from a discrete
distribution with $k$ outcomes. Let $\pi_1,\ldots,\pi_k$ be the
probabilities of each particular outcome. We are intereste... | Why does chi-square testing use the expected count as the variance?
Let's handle the simplest case to try to provide the most
intuition. Let $X_1, X_2, \ldots, X_n$ be an iid sample from a discrete
distribution with $k$ outcomes. Let $\pi_1,\ldots,\pi_k$ be the
probab |
15,306 | Why does chi-square testing use the expected count as the variance? | Why does chi-square testing use the expected count as the variance?
You can make the jump from standardized residuals$$\epsilon_i = \frac{O_i-E_i}{\sqrt{N(E_i/N)(1-E_i/N)}}$$ to the terms as used in the $\chi^2$ expression $$x_i = \frac{O_i-E_i}{\sqrt{E_i}}$$ This is the topic of the question Obtaining the chi-squared ... | Why does chi-square testing use the expected count as the variance? | Why does chi-square testing use the expected count as the variance?
You can make the jump from standardized residuals$$\epsilon_i = \frac{O_i-E_i}{\sqrt{N(E_i/N)(1-E_i/N)}}$$ to the terms as used in t | Why does chi-square testing use the expected count as the variance?
Why does chi-square testing use the expected count as the variance?
You can make the jump from standardized residuals$$\epsilon_i = \frac{O_i-E_i}{\sqrt{N(E_i/N)(1-E_i/N)}}$$ to the terms as used in the $\chi^2$ expression $$x_i = \frac{O_i-E_i}{\sqrt{... | Why does chi-square testing use the expected count as the variance?
Why does chi-square testing use the expected count as the variance?
You can make the jump from standardized residuals$$\epsilon_i = \frac{O_i-E_i}{\sqrt{N(E_i/N)(1-E_i/N)}}$$ to the terms as used in t |
15,307 | Conjoint Packages for R | I've never used R for conjoint analysis, but here are a couple of things I found when I hunted around.
Aizaki and Nishimura (2008) have an article "Design and Analysis of Choice Experiments Using R: A brief introduction" (Free PDF available here).
Perhaps check out the following packages:
AlgDesign for constructing ... | Conjoint Packages for R | I've never used R for conjoint analysis, but here are a couple of things I found when I hunted around.
Aizaki and Nishimura (2008) have an article "Design and Analysis of Choice Experiments Using R: | Conjoint Packages for R
I've never used R for conjoint analysis, but here are a couple of things I found when I hunted around.
Aizaki and Nishimura (2008) have an article "Design and Analysis of Choice Experiments Using R: A brief introduction" (Free PDF available here).
Perhaps check out the following packages:
Alg... | Conjoint Packages for R
I've never used R for conjoint analysis, but here are a couple of things I found when I hunted around.
Aizaki and Nishimura (2008) have an article "Design and Analysis of Choice Experiments Using R: |
15,308 | Conjoint Packages for R | mlogit is the best R package I've found for modelling discrete choice data. It supports the basic multinomial logit, as well as more advanced models such as multinomial probit and mixed logit. The package also includes specification tests to choose between different models. | Conjoint Packages for R | mlogit is the best R package I've found for modelling discrete choice data. It supports the basic multinomial logit, as well as more advanced models such as multinomial probit and mixed logit. The p | Conjoint Packages for R
mlogit is the best R package I've found for modelling discrete choice data. It supports the basic multinomial logit, as well as more advanced models such as multinomial probit and mixed logit. The package also includes specification tests to choose between different models. | Conjoint Packages for R
mlogit is the best R package I've found for modelling discrete choice data. It supports the basic multinomial logit, as well as more advanced models such as multinomial probit and mixed logit. The p |
15,309 | Conjoint Packages for R | You may want to use faisalconjoint package in R, it is tested with many published and research data, it works perfectly, one on important thing its works without design restriction and rank procedure. It works in all condition and provide accurate estimates. | Conjoint Packages for R | You may want to use faisalconjoint package in R, it is tested with many published and research data, it works perfectly, one on important thing its works without design restriction and rank procedure. | Conjoint Packages for R
You may want to use faisalconjoint package in R, it is tested with many published and research data, it works perfectly, one on important thing its works without design restriction and rank procedure. It works in all condition and provide accurate estimates. | Conjoint Packages for R
You may want to use faisalconjoint package in R, it is tested with many published and research data, it works perfectly, one on important thing its works without design restriction and rank procedure. |
15,310 | Conjoint Packages for R | The best in my opinion for R is a conjoint package from CRAN:
http://cran.r-project.org/web/packages/conjoint/index.html | Conjoint Packages for R | The best in my opinion for R is a conjoint package from CRAN:
http://cran.r-project.org/web/packages/conjoint/index.html | Conjoint Packages for R
The best in my opinion for R is a conjoint package from CRAN:
http://cran.r-project.org/web/packages/conjoint/index.html | Conjoint Packages for R
The best in my opinion for R is a conjoint package from CRAN:
http://cran.r-project.org/web/packages/conjoint/index.html |
15,311 | Conjoint Packages for R | If you are looking for models other than logit,
you can use 'survival' package to build conditional multinomial
logit model.
you can use 'bayesm' package to build hierarchical
bayesian(HB) model. Sawtoothsoftware asked the guy who created this
package to help them build HB model in their software. | Conjoint Packages for R | If you are looking for models other than logit,
you can use 'survival' package to build conditional multinomial
logit model.
you can use 'bayesm' package to build hierarchical
bayesian(HB) model. Sa | Conjoint Packages for R
If you are looking for models other than logit,
you can use 'survival' package to build conditional multinomial
logit model.
you can use 'bayesm' package to build hierarchical
bayesian(HB) model. Sawtoothsoftware asked the guy who created this
package to help them build HB model in their softw... | Conjoint Packages for R
If you are looking for models other than logit,
you can use 'survival' package to build conditional multinomial
logit model.
you can use 'bayesm' package to build hierarchical
bayesian(HB) model. Sa |
15,312 | Conjoint Packages for R | Faisal Conjoint Model (FCM) is an integrated model of conjoint
analysis and random utility models, developed by Faisal Afzal Sid-
diqui, Ghulam Hussain, and Mudassir Uddin in 2012. Its algorithm
was written in R statistical language and available in R [29]. Its
design is independent of design structure. It could be use... | Conjoint Packages for R | Faisal Conjoint Model (FCM) is an integrated model of conjoint
analysis and random utility models, developed by Faisal Afzal Sid-
diqui, Ghulam Hussain, and Mudassir Uddin in 2012. Its algorithm
was w | Conjoint Packages for R
Faisal Conjoint Model (FCM) is an integrated model of conjoint
analysis and random utility models, developed by Faisal Afzal Sid-
diqui, Ghulam Hussain, and Mudassir Uddin in 2012. Its algorithm
was written in R statistical language and available in R [29]. Its
design is independent of design st... | Conjoint Packages for R
Faisal Conjoint Model (FCM) is an integrated model of conjoint
analysis and random utility models, developed by Faisal Afzal Sid-
diqui, Ghulam Hussain, and Mudassir Uddin in 2012. Its algorithm
was w |
15,313 | Conjoint Packages for R | There is a library 'Conjoint' with many features and sample to find utilities.
For a quick preview check the link. This will help you get started.
https://rpubs.com/haj3/conjoint | Conjoint Packages for R | There is a library 'Conjoint' with many features and sample to find utilities.
For a quick preview check the link. This will help you get started.
https://rpubs.com/haj3/conjoint | Conjoint Packages for R
There is a library 'Conjoint' with many features and sample to find utilities.
For a quick preview check the link. This will help you get started.
https://rpubs.com/haj3/conjoint | Conjoint Packages for R
There is a library 'Conjoint' with many features and sample to find utilities.
For a quick preview check the link. This will help you get started.
https://rpubs.com/haj3/conjoint |
15,314 | Conjoint Packages for R | For R:
"survival" (clogit) for multinomial logit (MNL) model.
"mlogit" for a wide range of models (MNL, nested logit, heteroscedastic logit, mixed logit (MXL) also known as random parameters logit, ...).
In the same spirit you should take a look at "Rchoice" (file:///C:/Users/kruci/Downloads/v74i10.pdf).
"bayesm" for b... | Conjoint Packages for R | For R:
"survival" (clogit) for multinomial logit (MNL) model.
"mlogit" for a wide range of models (MNL, nested logit, heteroscedastic logit, mixed logit (MXL) also known as random parameters logit, .. | Conjoint Packages for R
For R:
"survival" (clogit) for multinomial logit (MNL) model.
"mlogit" for a wide range of models (MNL, nested logit, heteroscedastic logit, mixed logit (MXL) also known as random parameters logit, ...).
In the same spirit you should take a look at "Rchoice" (file:///C:/Users/kruci/Downloads/v74... | Conjoint Packages for R
For R:
"survival" (clogit) for multinomial logit (MNL) model.
"mlogit" for a wide range of models (MNL, nested logit, heteroscedastic logit, mixed logit (MXL) also known as random parameters logit, .. |
15,315 | Is using both training and test sets for hyperparameter tuning overfitting? | The idea behind holdout and cross validation is to estimate the generalization performance of a learning algorithm--that is, the expected performance on unknown/unseen data drawn from the same distribution as the training data. This can be used to tune hyperparameters or report the final performance. The validity of th... | Is using both training and test sets for hyperparameter tuning overfitting? | The idea behind holdout and cross validation is to estimate the generalization performance of a learning algorithm--that is, the expected performance on unknown/unseen data drawn from the same distrib | Is using both training and test sets for hyperparameter tuning overfitting?
The idea behind holdout and cross validation is to estimate the generalization performance of a learning algorithm--that is, the expected performance on unknown/unseen data drawn from the same distribution as the training data. This can be used... | Is using both training and test sets for hyperparameter tuning overfitting?
The idea behind holdout and cross validation is to estimate the generalization performance of a learning algorithm--that is, the expected performance on unknown/unseen data drawn from the same distrib |
15,316 | Is using both training and test sets for hyperparameter tuning overfitting? | Yes, you are overfitting. The test set should be used only for testing, not for parameter tuning. Searching for parameters on the test set will learn the rules that are present in the test set, and eventually overfit it. | Is using both training and test sets for hyperparameter tuning overfitting? | Yes, you are overfitting. The test set should be used only for testing, not for parameter tuning. Searching for parameters on the test set will learn the rules that are present in the test set, and ev | Is using both training and test sets for hyperparameter tuning overfitting?
Yes, you are overfitting. The test set should be used only for testing, not for parameter tuning. Searching for parameters on the test set will learn the rules that are present in the test set, and eventually overfit it. | Is using both training and test sets for hyperparameter tuning overfitting?
Yes, you are overfitting. The test set should be used only for testing, not for parameter tuning. Searching for parameters on the test set will learn the rules that are present in the test set, and ev |
15,317 | Is using both training and test sets for hyperparameter tuning overfitting? | I would say you are not necessarily overfitting, because overfitting is a term that is normally used to indicate that your model does not generalise well. E.g. If you would be doing linear regression on something like MNIST images, you are probably still underfitting (it does not generalise enough) when training on bot... | Is using both training and test sets for hyperparameter tuning overfitting? | I would say you are not necessarily overfitting, because overfitting is a term that is normally used to indicate that your model does not generalise well. E.g. If you would be doing linear regression | Is using both training and test sets for hyperparameter tuning overfitting?
I would say you are not necessarily overfitting, because overfitting is a term that is normally used to indicate that your model does not generalise well. E.g. If you would be doing linear regression on something like MNIST images, you are prob... | Is using both training and test sets for hyperparameter tuning overfitting?
I would say you are not necessarily overfitting, because overfitting is a term that is normally used to indicate that your model does not generalise well. E.g. If you would be doing linear regression |
15,318 | Is using both training and test sets for hyperparameter tuning overfitting? | It is not necessarily overfitting, but it also runs an unnecessary risk of overfitting, and you deprive yourself of the possibility to detect overfitting.
Overfitting happens when your model is too complex/has too many degrees of freedom for the available training data. This includes degrees of freedom for the hyperp... | Is using both training and test sets for hyperparameter tuning overfitting? | It is not necessarily overfitting, but it also runs an unnecessary risk of overfitting, and you deprive yourself of the possibility to detect overfitting.
Overfitting happens when your model is too | Is using both training and test sets for hyperparameter tuning overfitting?
It is not necessarily overfitting, but it also runs an unnecessary risk of overfitting, and you deprive yourself of the possibility to detect overfitting.
Overfitting happens when your model is too complex/has too many degrees of freedom for ... | Is using both training and test sets for hyperparameter tuning overfitting?
It is not necessarily overfitting, but it also runs an unnecessary risk of overfitting, and you deprive yourself of the possibility to detect overfitting.
Overfitting happens when your model is too |
15,319 | Is using both training and test sets for hyperparameter tuning overfitting? | It is an "in-sample" forecast since you eventually make the forecast on observations that are already part of your training set. Why not use n-fold cross-validation? By doing that, at each time, you are making "out-of" sample forecast, in which test set and training set are separate. | Is using both training and test sets for hyperparameter tuning overfitting? | It is an "in-sample" forecast since you eventually make the forecast on observations that are already part of your training set. Why not use n-fold cross-validation? By doing that, at each time, you a | Is using both training and test sets for hyperparameter tuning overfitting?
It is an "in-sample" forecast since you eventually make the forecast on observations that are already part of your training set. Why not use n-fold cross-validation? By doing that, at each time, you are making "out-of" sample forecast, in which... | Is using both training and test sets for hyperparameter tuning overfitting?
It is an "in-sample" forecast since you eventually make the forecast on observations that are already part of your training set. Why not use n-fold cross-validation? By doing that, at each time, you a |
15,320 | Is using both training and test sets for hyperparameter tuning overfitting? | The idea of CV is to overcome the weaknesses of Train-Test split (loss of information, only a part being used for testing etc.). Hence, CV ensures that all parts of data falls into training and testing folds in the successive iterations. This ensures that we get a balanced picture of whatever we are trying to evaluate ... | Is using both training and test sets for hyperparameter tuning overfitting? | The idea of CV is to overcome the weaknesses of Train-Test split (loss of information, only a part being used for testing etc.). Hence, CV ensures that all parts of data falls into training and testin | Is using both training and test sets for hyperparameter tuning overfitting?
The idea of CV is to overcome the weaknesses of Train-Test split (loss of information, only a part being used for testing etc.). Hence, CV ensures that all parts of data falls into training and testing folds in the successive iterations. This e... | Is using both training and test sets for hyperparameter tuning overfitting?
The idea of CV is to overcome the weaknesses of Train-Test split (loss of information, only a part being used for testing etc.). Hence, CV ensures that all parts of data falls into training and testin |
15,321 | In multiple linear regression, why does a plot of predicted points not lie in a straight line? | Suppose your multiple regression equation was
$$\hat y = 2 x_1 + 5 x_2 + 3$$
where $\hat y$ means "predicted $y$".
Now take just those points for which $x_2 = 1$. Then if you plot $\hat y$ against $x_1$, these points will satisfy the equation:
$$\hat y = 2 x_1 + 5(1) + 3 = 2 x_1 + 8$$
So they must lie on a line of slo... | In multiple linear regression, why does a plot of predicted points not lie in a straight line? | Suppose your multiple regression equation was
$$\hat y = 2 x_1 + 5 x_2 + 3$$
where $\hat y$ means "predicted $y$".
Now take just those points for which $x_2 = 1$. Then if you plot $\hat y$ against $x | In multiple linear regression, why does a plot of predicted points not lie in a straight line?
Suppose your multiple regression equation was
$$\hat y = 2 x_1 + 5 x_2 + 3$$
where $\hat y$ means "predicted $y$".
Now take just those points for which $x_2 = 1$. Then if you plot $\hat y$ against $x_1$, these points will sa... | In multiple linear regression, why does a plot of predicted points not lie in a straight line?
Suppose your multiple regression equation was
$$\hat y = 2 x_1 + 5 x_2 + 3$$
where $\hat y$ means "predicted $y$".
Now take just those points for which $x_2 = 1$. Then if you plot $\hat y$ against $x |
15,322 | Non-nested model selection | The LR (likelihood ratio) test actually is testing the hypothesis that a specified subset of the parameters equal some pre-specified values. In the case of model selection, generally (but not always) that means some of the parameters equal zero. If the models are nested, the parameters in the larger model that are no... | Non-nested model selection | The LR (likelihood ratio) test actually is testing the hypothesis that a specified subset of the parameters equal some pre-specified values. In the case of model selection, generally (but not always) | Non-nested model selection
The LR (likelihood ratio) test actually is testing the hypothesis that a specified subset of the parameters equal some pre-specified values. In the case of model selection, generally (but not always) that means some of the parameters equal zero. If the models are nested, the parameters in t... | Non-nested model selection
The LR (likelihood ratio) test actually is testing the hypothesis that a specified subset of the parameters equal some pre-specified values. In the case of model selection, generally (but not always) |
15,323 | Non-nested model selection | The derivation of AIC as an estimator of Kullback-Leibler information loss makes no assumptions of models being nested. | Non-nested model selection | The derivation of AIC as an estimator of Kullback-Leibler information loss makes no assumptions of models being nested. | Non-nested model selection
The derivation of AIC as an estimator of Kullback-Leibler information loss makes no assumptions of models being nested. | Non-nested model selection
The derivation of AIC as an estimator of Kullback-Leibler information loss makes no assumptions of models being nested. |
15,324 | Example when using accuracy as an outcome measure will lead to a wrong conclusion | I'll cheat.
Specifically, I have argued often (e.g., here) that the statistical part of modeling and prediction extends only to making probabilistic predictions for class memberships (or giving predictive densities, in the case of numerical forecasting). Treating a specific instance as if it belonged to a specific clas... | Example when using accuracy as an outcome measure will lead to a wrong conclusion | I'll cheat.
Specifically, I have argued often (e.g., here) that the statistical part of modeling and prediction extends only to making probabilistic predictions for class memberships (or giving predic | Example when using accuracy as an outcome measure will lead to a wrong conclusion
I'll cheat.
Specifically, I have argued often (e.g., here) that the statistical part of modeling and prediction extends only to making probabilistic predictions for class memberships (or giving predictive densities, in the case of numeric... | Example when using accuracy as an outcome measure will lead to a wrong conclusion
I'll cheat.
Specifically, I have argued often (e.g., here) that the statistical part of modeling and prediction extends only to making probabilistic predictions for class memberships (or giving predic |
15,325 | Example when using accuracy as an outcome measure will lead to a wrong conclusion | It might worth adding another, perhaps more straightforward example to Stephen's excellent answer.
Let's consider a medical test, the result of which is normally distributed, both in sick and in healthy people, with different parameters of course (but for simplicity, let's assume homoscedasticity, i.e., that the varian... | Example when using accuracy as an outcome measure will lead to a wrong conclusion | It might worth adding another, perhaps more straightforward example to Stephen's excellent answer.
Let's consider a medical test, the result of which is normally distributed, both in sick and in healt | Example when using accuracy as an outcome measure will lead to a wrong conclusion
It might worth adding another, perhaps more straightforward example to Stephen's excellent answer.
Let's consider a medical test, the result of which is normally distributed, both in sick and in healthy people, with different parameters o... | Example when using accuracy as an outcome measure will lead to a wrong conclusion
It might worth adding another, perhaps more straightforward example to Stephen's excellent answer.
Let's consider a medical test, the result of which is normally distributed, both in sick and in healt |
15,326 | Why is Ordinary Least Squares performing better than Poisson regression? | I suspect that part of the problem may lie in your choice of performance metric. If you measure test performance using RMSE then training the model to minimise the MSE matches the test criterion, giving a hint as to what is considered important. You may find that if you measure the test performance using the negative... | Why is Ordinary Least Squares performing better than Poisson regression? | I suspect that part of the problem may lie in your choice of performance metric. If you measure test performance using RMSE then training the model to minimise the MSE matches the test criterion, giv | Why is Ordinary Least Squares performing better than Poisson regression?
I suspect that part of the problem may lie in your choice of performance metric. If you measure test performance using RMSE then training the model to minimise the MSE matches the test criterion, giving a hint as to what is considered important. ... | Why is Ordinary Least Squares performing better than Poisson regression?
I suspect that part of the problem may lie in your choice of performance metric. If you measure test performance using RMSE then training the model to minimise the MSE matches the test criterion, giv |
15,327 | Why is Ordinary Least Squares performing better than Poisson regression? | First, with such data I would expect overdispersion (if you don't know what that is, see https://stats.stackexchange.com/search?q=what+is+overdispersion%3F ).
That would have to be addressed with a Poisson glm, but is not an issue with usual linear regression. As said in a comment, with a poisson glm you want to incl... | Why is Ordinary Least Squares performing better than Poisson regression? | First, with such data I would expect overdispersion (if you don't know what that is, see https://stats.stackexchange.com/search?q=what+is+overdispersion%3F ).
That would have to be addressed with a P | Why is Ordinary Least Squares performing better than Poisson regression?
First, with such data I would expect overdispersion (if you don't know what that is, see https://stats.stackexchange.com/search?q=what+is+overdispersion%3F ).
That would have to be addressed with a Poisson glm, but is not an issue with usual line... | Why is Ordinary Least Squares performing better than Poisson regression?
First, with such data I would expect overdispersion (if you don't know what that is, see https://stats.stackexchange.com/search?q=what+is+overdispersion%3F ).
That would have to be addressed with a P |
15,328 | Why is Ordinary Least Squares performing better than Poisson regression? | There are lots of choices of pseudo $R^2$'s. Lots of them are very flawed. Generally speaking, there's usually no reason that the $R^2$ produced from OLS will be a comparable value to a given pseudo $R^2$; rather, pseudo $R^2$'s are typically used for comparing models of the same distributional family. | Why is Ordinary Least Squares performing better than Poisson regression? | There are lots of choices of pseudo $R^2$'s. Lots of them are very flawed. Generally speaking, there's usually no reason that the $R^2$ produced from OLS will be a comparable value to a given pseudo $ | Why is Ordinary Least Squares performing better than Poisson regression?
There are lots of choices of pseudo $R^2$'s. Lots of them are very flawed. Generally speaking, there's usually no reason that the $R^2$ produced from OLS will be a comparable value to a given pseudo $R^2$; rather, pseudo $R^2$'s are typically used... | Why is Ordinary Least Squares performing better than Poisson regression?
There are lots of choices of pseudo $R^2$'s. Lots of them are very flawed. Generally speaking, there's usually no reason that the $R^2$ produced from OLS will be a comparable value to a given pseudo $ |
15,329 | Why is Ordinary Least Squares performing better than Poisson regression? | It is true that your data is not Normally distributed (which I presume is why you also ran a Poisson regression) but your data is likely not a Poisson distribution either. The Poisson distribution assumes that the mean and the variance are the same, which is likely not the case (as mentioned in other answers - you can ... | Why is Ordinary Least Squares performing better than Poisson regression? | It is true that your data is not Normally distributed (which I presume is why you also ran a Poisson regression) but your data is likely not a Poisson distribution either. The Poisson distribution ass | Why is Ordinary Least Squares performing better than Poisson regression?
It is true that your data is not Normally distributed (which I presume is why you also ran a Poisson regression) but your data is likely not a Poisson distribution either. The Poisson distribution assumes that the mean and the variance are the sam... | Why is Ordinary Least Squares performing better than Poisson regression?
It is true that your data is not Normally distributed (which I presume is why you also ran a Poisson regression) but your data is likely not a Poisson distribution either. The Poisson distribution ass |
15,330 | Confidence intervals for median | Summary
When you can assume little or nothing about the true probability law, and can infer little about it--which is the case for small samples of $n$ observations--then a suitably chosen pair of order statistics will constitute a confidence interval for the median. Which order statistics to choose can easily be foun... | Confidence intervals for median | Summary
When you can assume little or nothing about the true probability law, and can infer little about it--which is the case for small samples of $n$ observations--then a suitably chosen pair of ord | Confidence intervals for median
Summary
When you can assume little or nothing about the true probability law, and can infer little about it--which is the case for small samples of $n$ observations--then a suitably chosen pair of order statistics will constitute a confidence interval for the median. Which order statist... | Confidence intervals for median
Summary
When you can assume little or nothing about the true probability law, and can infer little about it--which is the case for small samples of $n$ observations--then a suitably chosen pair of ord |
15,331 | Confidence intervals for median | If you wish to use numerical methods, you may generate an estimate of the samping distribution of medians by using bootstrap. Repeatedly resample your sample and compute many medians. The stdev of these medians serves as an estimate of the stdev of the sampling distribution of medians. I used a similar method to comput... | Confidence intervals for median | If you wish to use numerical methods, you may generate an estimate of the samping distribution of medians by using bootstrap. Repeatedly resample your sample and compute many medians. The stdev of the | Confidence intervals for median
If you wish to use numerical methods, you may generate an estimate of the samping distribution of medians by using bootstrap. Repeatedly resample your sample and compute many medians. The stdev of these medians serves as an estimate of the stdev of the sampling distribution of medians. I... | Confidence intervals for median
If you wish to use numerical methods, you may generate an estimate of the samping distribution of medians by using bootstrap. Repeatedly resample your sample and compute many medians. The stdev of the |
15,332 | Why don't we use significant digits? | Significant digits are used in some fields (I learned about them in Chemistry) to indicate the degree of meaningful precision that exists in a number. This is an important topic in statistics as well, so in fact we report this constantly--we just report it in a different form. Specifically, we report confidence inter... | Why don't we use significant digits? | Significant digits are used in some fields (I learned about them in Chemistry) to indicate the degree of meaningful precision that exists in a number. This is an important topic in statistics as well | Why don't we use significant digits?
Significant digits are used in some fields (I learned about them in Chemistry) to indicate the degree of meaningful precision that exists in a number. This is an important topic in statistics as well, so in fact we report this constantly--we just report it in a different form. Spe... | Why don't we use significant digits?
Significant digits are used in some fields (I learned about them in Chemistry) to indicate the degree of meaningful precision that exists in a number. This is an important topic in statistics as well |
15,333 | Why don't we use significant digits? | One reason for restricting the number of digits reported in many estimates, p-values, etc. is based on perception. Reporting something like p = 0.04872429 implies a level of precision in the results that causes them to be perceived as more accurate.
Essentially, the use of high numbers of digits in reporting statistica... | Why don't we use significant digits? | One reason for restricting the number of digits reported in many estimates, p-values, etc. is based on perception. Reporting something like p = 0.04872429 implies a level of precision in the results t | Why don't we use significant digits?
One reason for restricting the number of digits reported in many estimates, p-values, etc. is based on perception. Reporting something like p = 0.04872429 implies a level of precision in the results that causes them to be perceived as more accurate.
Essentially, the use of high numb... | Why don't we use significant digits?
One reason for restricting the number of digits reported in many estimates, p-values, etc. is based on perception. Reporting something like p = 0.04872429 implies a level of precision in the results t |
15,334 | Why don't we use significant digits? | Are you talking about rounding your data to some number of significant digits or rounding your final answer? If you round your data you can get into situations where you've thrown away noise that statistical calculations need to use. | Why don't we use significant digits? | Are you talking about rounding your data to some number of significant digits or rounding your final answer? If you round your data you can get into situations where you've thrown away noise that sta | Why don't we use significant digits?
Are you talking about rounding your data to some number of significant digits or rounding your final answer? If you round your data you can get into situations where you've thrown away noise that statistical calculations need to use. | Why don't we use significant digits?
Are you talking about rounding your data to some number of significant digits or rounding your final answer? If you round your data you can get into situations where you've thrown away noise that sta |
15,335 | Why don't we use significant digits? | I think it really depends upon the level of confidence required, fewer digits for significance are appropriate for 95%, as opposed to 99.999% or greater, for example, as used by CERN for many of their results. | Why don't we use significant digits? | I think it really depends upon the level of confidence required, fewer digits for significance are appropriate for 95%, as opposed to 99.999% or greater, for example, as used by CERN for many of their | Why don't we use significant digits?
I think it really depends upon the level of confidence required, fewer digits for significance are appropriate for 95%, as opposed to 99.999% or greater, for example, as used by CERN for many of their results. | Why don't we use significant digits?
I think it really depends upon the level of confidence required, fewer digits for significance are appropriate for 95%, as opposed to 99.999% or greater, for example, as used by CERN for many of their |
15,336 | Examples of processes that are not Poisson? | Number of cigarettes smoked in a period of time: this requires a zero-inflated process (e.g. zero-inflated Poisson or zero-inflated negative binomial) because not everyone smokes cigarettes. | Examples of processes that are not Poisson? | Number of cigarettes smoked in a period of time: this requires a zero-inflated process (e.g. zero-inflated Poisson or zero-inflated negative binomial) because not everyone smokes cigarettes. | Examples of processes that are not Poisson?
Number of cigarettes smoked in a period of time: this requires a zero-inflated process (e.g. zero-inflated Poisson or zero-inflated negative binomial) because not everyone smokes cigarettes. | Examples of processes that are not Poisson?
Number of cigarettes smoked in a period of time: this requires a zero-inflated process (e.g. zero-inflated Poisson or zero-inflated negative binomial) because not everyone smokes cigarettes. |
15,337 | Examples of processes that are not Poisson? | Do you mean positive count data? Unbounded?
The negative binomial is popular.
Another good model is the Poisson with inflated 0. That model assumes that either something is happening or it isn't - and if it is, it follows a Poisson. I saw an example recently. Nurses who treated AIDS patients were asked how often they ... | Examples of processes that are not Poisson? | Do you mean positive count data? Unbounded?
The negative binomial is popular.
Another good model is the Poisson with inflated 0. That model assumes that either something is happening or it isn't - an | Examples of processes that are not Poisson?
Do you mean positive count data? Unbounded?
The negative binomial is popular.
Another good model is the Poisson with inflated 0. That model assumes that either something is happening or it isn't - and if it is, it follows a Poisson. I saw an example recently. Nurses who trea... | Examples of processes that are not Poisson?
Do you mean positive count data? Unbounded?
The negative binomial is popular.
Another good model is the Poisson with inflated 0. That model assumes that either something is happening or it isn't - an |
15,338 | Examples of processes that are not Poisson? | Counting processes that aren't Poisson? Well, any finite sample space process like binomial or discrete uniform. You get a Poisson counting process from counting events having independent interarrival times which are exponentially distributed, so a whole host of generalizations fall out of that such as having gamma or ... | Examples of processes that are not Poisson? | Counting processes that aren't Poisson? Well, any finite sample space process like binomial or discrete uniform. You get a Poisson counting process from counting events having independent interarrival | Examples of processes that are not Poisson?
Counting processes that aren't Poisson? Well, any finite sample space process like binomial or discrete uniform. You get a Poisson counting process from counting events having independent interarrival times which are exponentially distributed, so a whole host of generalizatio... | Examples of processes that are not Poisson?
Counting processes that aren't Poisson? Well, any finite sample space process like binomial or discrete uniform. You get a Poisson counting process from counting events having independent interarrival |
15,339 | Examples of processes that are not Poisson? | It's unclear if you want counting processes or not.
If I interpret the 'teaching' tag to mean you are teaching the Poisson process then, for teaching about a process in general, the Bernoulli process is an easy random process to explain and visualize and is related to the Poisson process. The Bernoulli process is the ... | Examples of processes that are not Poisson? | It's unclear if you want counting processes or not.
If I interpret the 'teaching' tag to mean you are teaching the Poisson process then, for teaching about a process in general, the Bernoulli process | Examples of processes that are not Poisson?
It's unclear if you want counting processes or not.
If I interpret the 'teaching' tag to mean you are teaching the Poisson process then, for teaching about a process in general, the Bernoulli process is an easy random process to explain and visualize and is related to the Po... | Examples of processes that are not Poisson?
It's unclear if you want counting processes or not.
If I interpret the 'teaching' tag to mean you are teaching the Poisson process then, for teaching about a process in general, the Bernoulli process |
15,340 | Examples of processes that are not Poisson? | As an property/casualty actuary, I deal with real-life examples of discrete processes which are non-Poisson all the time. For high-severity, low-frequency lines of business, the Poisson distribution is ill-suited as it demands a variance-to-mean ratio of 1. The negative binomial distribution, mentioned above, is much m... | Examples of processes that are not Poisson? | As an property/casualty actuary, I deal with real-life examples of discrete processes which are non-Poisson all the time. For high-severity, low-frequency lines of business, the Poisson distribution i | Examples of processes that are not Poisson?
As an property/casualty actuary, I deal with real-life examples of discrete processes which are non-Poisson all the time. For high-severity, low-frequency lines of business, the Poisson distribution is ill-suited as it demands a variance-to-mean ratio of 1. The negative binom... | Examples of processes that are not Poisson?
As an property/casualty actuary, I deal with real-life examples of discrete processes which are non-Poisson all the time. For high-severity, low-frequency lines of business, the Poisson distribution i |
15,341 | Examples of processes that are not Poisson? | Others have mentioned several examples of point process that are not Poisson. Because the Poisson corresponds to exponential interarrival times if you pick any interarrival time distribution that is no exponential the resulting point process is not Poisson. AdamO pointed out the Weibull. You could use gamma, lognorma... | Examples of processes that are not Poisson? | Others have mentioned several examples of point process that are not Poisson. Because the Poisson corresponds to exponential interarrival times if you pick any interarrival time distribution that is | Examples of processes that are not Poisson?
Others have mentioned several examples of point process that are not Poisson. Because the Poisson corresponds to exponential interarrival times if you pick any interarrival time distribution that is no exponential the resulting point process is not Poisson. AdamO pointed out... | Examples of processes that are not Poisson?
Others have mentioned several examples of point process that are not Poisson. Because the Poisson corresponds to exponential interarrival times if you pick any interarrival time distribution that is |
15,342 | Examples of processes that are not Poisson? | Another interesting example of non-Poisson counting process is represented by the zero-truncated Poisson distribution (ZTPD). ZTPD can fit data concerning the number of languages subjects can speak in physiological conditions. In this instance, Poisson distribution is ill-behaving, because the number of spoken language... | Examples of processes that are not Poisson? | Another interesting example of non-Poisson counting process is represented by the zero-truncated Poisson distribution (ZTPD). ZTPD can fit data concerning the number of languages subjects can speak in | Examples of processes that are not Poisson?
Another interesting example of non-Poisson counting process is represented by the zero-truncated Poisson distribution (ZTPD). ZTPD can fit data concerning the number of languages subjects can speak in physiological conditions. In this instance, Poisson distribution is ill-beh... | Examples of processes that are not Poisson?
Another interesting example of non-Poisson counting process is represented by the zero-truncated Poisson distribution (ZTPD). ZTPD can fit data concerning the number of languages subjects can speak in |
15,343 | Examples of processes that are not Poisson? | I believe that you could take your customer-arrival Poisson process and tweak it in two different ways: 1) customer arrivals are measured 24-hours a day, but the store is not actually open all day, and 2) imagine two competing stores with Poisson process customer arrival times and look at the difference between the arr... | Examples of processes that are not Poisson? | I believe that you could take your customer-arrival Poisson process and tweak it in two different ways: 1) customer arrivals are measured 24-hours a day, but the store is not actually open all day, an | Examples of processes that are not Poisson?
I believe that you could take your customer-arrival Poisson process and tweak it in two different ways: 1) customer arrivals are measured 24-hours a day, but the store is not actually open all day, and 2) imagine two competing stores with Poisson process customer arrival time... | Examples of processes that are not Poisson?
I believe that you could take your customer-arrival Poisson process and tweak it in two different ways: 1) customer arrivals are measured 24-hours a day, but the store is not actually open all day, an |
15,344 | Examples of processes that are not Poisson? | You might want to reconsider the soccer example. It seems that the scoring rates for both teams increase as the match goes on, & that they change when teams change their attacking/defending priorities in response to the current score.
Or rather, use it as an example of how simple models can perform surprisingly well, s... | Examples of processes that are not Poisson? | You might want to reconsider the soccer example. It seems that the scoring rates for both teams increase as the match goes on, & that they change when teams change their attacking/defending priorities | Examples of processes that are not Poisson?
You might want to reconsider the soccer example. It seems that the scoring rates for both teams increase as the match goes on, & that they change when teams change their attacking/defending priorities in response to the current score.
Or rather, use it as an example of how si... | Examples of processes that are not Poisson?
You might want to reconsider the soccer example. It seems that the scoring rates for both teams increase as the match goes on, & that they change when teams change their attacking/defending priorities |
15,345 | Examples of processes that are not Poisson? | Since the question is related to making the Poisson distribution more understandable, I'll give it a go, since I recently looked into this somewhat for call center incoming call patterns (which follow a memory-less, exponential distribution as time goes on).
I think delving into another tangential model that essentiall... | Examples of processes that are not Poisson? | Since the question is related to making the Poisson distribution more understandable, I'll give it a go, since I recently looked into this somewhat for call center incoming call patterns (which follow | Examples of processes that are not Poisson?
Since the question is related to making the Poisson distribution more understandable, I'll give it a go, since I recently looked into this somewhat for call center incoming call patterns (which follow a memory-less, exponential distribution as time goes on).
I think delving i... | Examples of processes that are not Poisson?
Since the question is related to making the Poisson distribution more understandable, I'll give it a go, since I recently looked into this somewhat for call center incoming call patterns (which follow |
15,346 | Examples of processes that are not Poisson? | Number of visits by an individual customer to the grocery store within a given time interval.
After you have been to the grocery store, you are unlikely to return for a while unless you made a planning mistake.
I think the Negative Binomial distribution could be used here, but it is discrete, whereas the visits are in ... | Examples of processes that are not Poisson? | Number of visits by an individual customer to the grocery store within a given time interval.
After you have been to the grocery store, you are unlikely to return for a while unless you made a plannin | Examples of processes that are not Poisson?
Number of visits by an individual customer to the grocery store within a given time interval.
After you have been to the grocery store, you are unlikely to return for a while unless you made a planning mistake.
I think the Negative Binomial distribution could be used here, bu... | Examples of processes that are not Poisson?
Number of visits by an individual customer to the grocery store within a given time interval.
After you have been to the grocery store, you are unlikely to return for a while unless you made a plannin |
15,347 | How to correctly word a frequentist confidence interval | There are various ways you can reasonably word a confidence interval statement, but any variation on the statements below would be fine. (Since you did not specify to the contrary, I am assuming that this was a 95% confidence interval. If not then you should make the appropriate changes in the statement.) What is im... | How to correctly word a frequentist confidence interval | There are various ways you can reasonably word a confidence interval statement, but any variation on the statements below would be fine. (Since you did not specify to the contrary, I am assuming that | How to correctly word a frequentist confidence interval
There are various ways you can reasonably word a confidence interval statement, but any variation on the statements below would be fine. (Since you did not specify to the contrary, I am assuming that this was a 95% confidence interval. If not then you should mak... | How to correctly word a frequentist confidence interval
There are various ways you can reasonably word a confidence interval statement, but any variation on the statements below would be fine. (Since you did not specify to the contrary, I am assuming that |
15,348 | How to correctly word a frequentist confidence interval | I agree with Ben that normally, you would just say, "Our estimate of the slope is $\hat{\beta}=3.4\ (95\%\ {\rm CI}=[0.5,5.6])$."
If you really need to explain to someone what a confidence interval is, I would probably not use your formulation, "If we ran many experiments 95% of the 95% intervals constructed would cont... | How to correctly word a frequentist confidence interval | I agree with Ben that normally, you would just say, "Our estimate of the slope is $\hat{\beta}=3.4\ (95\%\ {\rm CI}=[0.5,5.6])$."
If you really need to explain to someone what a confidence interval is | How to correctly word a frequentist confidence interval
I agree with Ben that normally, you would just say, "Our estimate of the slope is $\hat{\beta}=3.4\ (95\%\ {\rm CI}=[0.5,5.6])$."
If you really need to explain to someone what a confidence interval is, I would probably not use your formulation, "If we ran many exp... | How to correctly word a frequentist confidence interval
I agree with Ben that normally, you would just say, "Our estimate of the slope is $\hat{\beta}=3.4\ (95\%\ {\rm CI}=[0.5,5.6])$."
If you really need to explain to someone what a confidence interval is |
15,349 | How to correctly word a frequentist confidence interval | This post is such a goldmine if information. Its worth a read on its own.
Confidence interval interpretation is a practice in the Precision-Usefulness trade off: You can state what a confidence interval is precisely, and it will not be useful. As you relax the precision, it becomes more and more useful. However, this... | How to correctly word a frequentist confidence interval | This post is such a goldmine if information. Its worth a read on its own.
Confidence interval interpretation is a practice in the Precision-Usefulness trade off: You can state what a confidence interv | How to correctly word a frequentist confidence interval
This post is such a goldmine if information. Its worth a read on its own.
Confidence interval interpretation is a practice in the Precision-Usefulness trade off: You can state what a confidence interval is precisely, and it will not be useful. As you relax the pr... | How to correctly word a frequentist confidence interval
This post is such a goldmine if information. Its worth a read on its own.
Confidence interval interpretation is a practice in the Precision-Usefulness trade off: You can state what a confidence interv |
15,350 | How to correctly word a frequentist confidence interval | How about:
"A procedure which constructs an interval from an experiment, having the property that the interval constructed by the procedure contains the true value of $\beta$ in 95% of cases, was applied to this experiment, for which the estimated value of $\beta$ was $\widehat{\beta} = 3.4$. The interval constructed b... | How to correctly word a frequentist confidence interval | How about:
"A procedure which constructs an interval from an experiment, having the property that the interval constructed by the procedure contains the true value of $\beta$ in 95% of cases, was appl | How to correctly word a frequentist confidence interval
How about:
"A procedure which constructs an interval from an experiment, having the property that the interval constructed by the procedure contains the true value of $\beta$ in 95% of cases, was applied to this experiment, for which the estimated value of $\beta$... | How to correctly word a frequentist confidence interval
How about:
"A procedure which constructs an interval from an experiment, having the property that the interval constructed by the procedure contains the true value of $\beta$ in 95% of cases, was appl |
15,351 | Interpretation of non-significant results as "trends" | This is a great question; the answer depends a lot on context.
In general I would say you are right: making an unqualified general claim like "group A used X more often than group B" is misleading. It would be better to say something like
in our experiment group A used X more often than group B, but we're very uncerta... | Interpretation of non-significant results as "trends" | This is a great question; the answer depends a lot on context.
In general I would say you are right: making an unqualified general claim like "group A used X more often than group B" is misleading. It | Interpretation of non-significant results as "trends"
This is a great question; the answer depends a lot on context.
In general I would say you are right: making an unqualified general claim like "group A used X more often than group B" is misleading. It would be better to say something like
in our experiment group A ... | Interpretation of non-significant results as "trends"
This is a great question; the answer depends a lot on context.
In general I would say you are right: making an unqualified general claim like "group A used X more often than group B" is misleading. It |
15,352 | Interpretation of non-significant results as "trends" | That's a tough question!
First things first, any threshold you may choose to determine statistical significance is arbitrary. The fact that most people use a $5\%$ $p$-value does not make it more correct than any other. So, in some sense, you should think of statistical significance as a "spectrum" rather than a black-... | Interpretation of non-significant results as "trends" | That's a tough question!
First things first, any threshold you may choose to determine statistical significance is arbitrary. The fact that most people use a $5\%$ $p$-value does not make it more corr | Interpretation of non-significant results as "trends"
That's a tough question!
First things first, any threshold you may choose to determine statistical significance is arbitrary. The fact that most people use a $5\%$ $p$-value does not make it more correct than any other. So, in some sense, you should think of statist... | Interpretation of non-significant results as "trends"
That's a tough question!
First things first, any threshold you may choose to determine statistical significance is arbitrary. The fact that most people use a $5\%$ $p$-value does not make it more corr |
15,353 | Interpretation of non-significant results as "trends" | Significant effect just means that you measured an unlikely anomaly (unlikely if the null hypothesis, absence of effect, would be true). And as a consequence it must be doubted with high probability (although this probability is not equal to the p-value and also depends on prior believes).
Depending on the quality of ... | Interpretation of non-significant results as "trends" | Significant effect just means that you measured an unlikely anomaly (unlikely if the null hypothesis, absence of effect, would be true). And as a consequence it must be doubted with high probability ( | Interpretation of non-significant results as "trends"
Significant effect just means that you measured an unlikely anomaly (unlikely if the null hypothesis, absence of effect, would be true). And as a consequence it must be doubted with high probability (although this probability is not equal to the p-value and also dep... | Interpretation of non-significant results as "trends"
Significant effect just means that you measured an unlikely anomaly (unlikely if the null hypothesis, absence of effect, would be true). And as a consequence it must be doubted with high probability ( |
15,354 | Interpretation of non-significant results as "trends" | It sounds like they're arguing p-value vs. the definition of "Trend".
If you plot the data out on a run chart, you may see a trend... a run of plot points that show a trend going up or down over time.
But, when you do the statistics on it.. the p-value suggests it's not significant.
For the p-value to show little signi... | Interpretation of non-significant results as "trends" | It sounds like they're arguing p-value vs. the definition of "Trend".
If you plot the data out on a run chart, you may see a trend... a run of plot points that show a trend going up or down over time. | Interpretation of non-significant results as "trends"
It sounds like they're arguing p-value vs. the definition of "Trend".
If you plot the data out on a run chart, you may see a trend... a run of plot points that show a trend going up or down over time.
But, when you do the statistics on it.. the p-value suggests it's... | Interpretation of non-significant results as "trends"
It sounds like they're arguing p-value vs. the definition of "Trend".
If you plot the data out on a run chart, you may see a trend... a run of plot points that show a trend going up or down over time. |
15,355 | Interpretation of non-significant results as "trends" | It sounds like in this case they have little justification for their claim and are just abusing statistics to reach the conclusion they already had. But there are times when it's ok to not be so strict with p-val cutoffs. This (how to use statistical significance and pval cutoffs) is a debate that has been raging since... | Interpretation of non-significant results as "trends" | It sounds like in this case they have little justification for their claim and are just abusing statistics to reach the conclusion they already had. But there are times when it's ok to not be so stric | Interpretation of non-significant results as "trends"
It sounds like in this case they have little justification for their claim and are just abusing statistics to reach the conclusion they already had. But there are times when it's ok to not be so strict with p-val cutoffs. This (how to use statistical significance an... | Interpretation of non-significant results as "trends"
It sounds like in this case they have little justification for their claim and are just abusing statistics to reach the conclusion they already had. But there are times when it's ok to not be so stric |
15,356 | How is adding noise to training data equivalent to regularization? | Adding noise to the regressors in the training data is similar to regularization because it leads to similar results to shrinkage.
The linear regression is an interesting example. Suppose $(Y_i,X_i)_{i=1}^n$ is a set of i.i.d. observations and that
$$ Y_i = \beta_0 + \beta_1X_i + U_i \qquad \mathbb{E}[U_i \mid X_i] = 0... | How is adding noise to training data equivalent to regularization? | Adding noise to the regressors in the training data is similar to regularization because it leads to similar results to shrinkage.
The linear regression is an interesting example. Suppose $(Y_i,X_i)_{ | How is adding noise to training data equivalent to regularization?
Adding noise to the regressors in the training data is similar to regularization because it leads to similar results to shrinkage.
The linear regression is an interesting example. Suppose $(Y_i,X_i)_{i=1}^n$ is a set of i.i.d. observations and that
$$ Y... | How is adding noise to training data equivalent to regularization?
Adding noise to the regressors in the training data is similar to regularization because it leads to similar results to shrinkage.
The linear regression is an interesting example. Suppose $(Y_i,X_i)_{ |
15,357 | How is adding noise to training data equivalent to regularization? | Overview: For linear regression, I'll show that $\ell_2$ regularization (a.k.a. ridge regression) arises from minimizing the expected squared error over random perturbations of the regressors. The distributional form of the perturbations doesn't matter beyond some minimal requirements (i.i.d., zero mean). The variance ... | How is adding noise to training data equivalent to regularization? | Overview: For linear regression, I'll show that $\ell_2$ regularization (a.k.a. ridge regression) arises from minimizing the expected squared error over random perturbations of the regressors. The dis | How is adding noise to training data equivalent to regularization?
Overview: For linear regression, I'll show that $\ell_2$ regularization (a.k.a. ridge regression) arises from minimizing the expected squared error over random perturbations of the regressors. The distributional form of the perturbations doesn't matter ... | How is adding noise to training data equivalent to regularization?
Overview: For linear regression, I'll show that $\ell_2$ regularization (a.k.a. ridge regression) arises from minimizing the expected squared error over random perturbations of the regressors. The dis |
15,358 | How is adding noise to training data equivalent to regularization? | The basic concept behind regularization is that we start with our Bayesian prior for the coefficients being a decreasing function of the magnitude of the coefficient. That is, the Bayesian prior for the coefficient being large is smaller than the prior for the coefficient being small. If the basic loss function gives a... | How is adding noise to training data equivalent to regularization? | The basic concept behind regularization is that we start with our Bayesian prior for the coefficients being a decreasing function of the magnitude of the coefficient. That is, the Bayesian prior for t | How is adding noise to training data equivalent to regularization?
The basic concept behind regularization is that we start with our Bayesian prior for the coefficients being a decreasing function of the magnitude of the coefficient. That is, the Bayesian prior for the coefficient being large is smaller than the prior ... | How is adding noise to training data equivalent to regularization?
The basic concept behind regularization is that we start with our Bayesian prior for the coefficients being a decreasing function of the magnitude of the coefficient. That is, the Bayesian prior for t |
15,359 | In regression, why not use regularization by default? | In short, regularization changes the distribution of the test statistic, rendering tests of hypothesis moot. In instances where we want to use regression to make inferences about interventions, we want unbiasedness.
Not everything to do with data is a prediction problem. | In regression, why not use regularization by default? | In short, regularization changes the distribution of the test statistic, rendering tests of hypothesis moot. In instances where we want to use regression to make inferences about interventions, we wa | In regression, why not use regularization by default?
In short, regularization changes the distribution of the test statistic, rendering tests of hypothesis moot. In instances where we want to use regression to make inferences about interventions, we want unbiasedness.
Not everything to do with data is a prediction pr... | In regression, why not use regularization by default?
In short, regularization changes the distribution of the test statistic, rendering tests of hypothesis moot. In instances where we want to use regression to make inferences about interventions, we wa |
15,360 | In regression, why not use regularization by default? | People often assume that regularization is superior to un-regularized models because they reduce multicollinearity, reduce model overfitting, and improve forecasting. They also like regularization because it explicitly avoid the entire body of model testing associated with the Gauss-Markov Theorem and other related un... | In regression, why not use regularization by default? | People often assume that regularization is superior to un-regularized models because they reduce multicollinearity, reduce model overfitting, and improve forecasting. They also like regularization be | In regression, why not use regularization by default?
People often assume that regularization is superior to un-regularized models because they reduce multicollinearity, reduce model overfitting, and improve forecasting. They also like regularization because it explicitly avoid the entire body of model testing associa... | In regression, why not use regularization by default?
People often assume that regularization is superior to un-regularized models because they reduce multicollinearity, reduce model overfitting, and improve forecasting. They also like regularization be |
15,361 | In regression, why not use regularization by default? | My comment would be that all boils down to assumptions. While we would like a hard and fast rule to everything, the world is at the very least a little more complicated than this. Blindly applying either is bound to mislead our interpretation. While we cannot test if the data fits every possible model or assumption, ... | In regression, why not use regularization by default? | My comment would be that all boils down to assumptions. While we would like a hard and fast rule to everything, the world is at the very least a little more complicated than this. Blindly applying e | In regression, why not use regularization by default?
My comment would be that all boils down to assumptions. While we would like a hard and fast rule to everything, the world is at the very least a little more complicated than this. Blindly applying either is bound to mislead our interpretation. While we cannot test... | In regression, why not use regularization by default?
My comment would be that all boils down to assumptions. While we would like a hard and fast rule to everything, the world is at the very least a little more complicated than this. Blindly applying e |
15,362 | In regression, why not use regularization by default? | another issue is that regression is often used to control for effects of other variables. Lets say I want to know if A is related to Y controlling for B, A and B are strongly correlated and my answer is no, however if I regularize A and B coefficients then my answer will be yes, which is wrong. | In regression, why not use regularization by default? | another issue is that regression is often used to control for effects of other variables. Lets say I want to know if A is related to Y controlling for B, A and B are strongly correlated and my answer | In regression, why not use regularization by default?
another issue is that regression is often used to control for effects of other variables. Lets say I want to know if A is related to Y controlling for B, A and B are strongly correlated and my answer is no, however if I regularize A and B coefficients then my answer... | In regression, why not use regularization by default?
another issue is that regression is often used to control for effects of other variables. Lets say I want to know if A is related to Y controlling for B, A and B are strongly correlated and my answer |
15,363 | Why not use the "normal equations" to find simple least squares coefficients? | For the problem $Ax \approx b$, forming the Normal equations squares the condition number of $A$ by forming $A^TA$. Roughly speaking $log_{10}(cond)$ is the number of digits you lose in your calculation if everything is done well. And this doesn't really have anything to do with forming the inverse of $A^TA$. No matte... | Why not use the "normal equations" to find simple least squares coefficients? | For the problem $Ax \approx b$, forming the Normal equations squares the condition number of $A$ by forming $A^TA$. Roughly speaking $log_{10}(cond)$ is the number of digits you lose in your calculati | Why not use the "normal equations" to find simple least squares coefficients?
For the problem $Ax \approx b$, forming the Normal equations squares the condition number of $A$ by forming $A^TA$. Roughly speaking $log_{10}(cond)$ is the number of digits you lose in your calculation if everything is done well. And this do... | Why not use the "normal equations" to find simple least squares coefficients?
For the problem $Ax \approx b$, forming the Normal equations squares the condition number of $A$ by forming $A^TA$. Roughly speaking $log_{10}(cond)$ is the number of digits you lose in your calculati |
15,364 | Why not use the "normal equations" to find simple least squares coefficients? | If you only have to solve this one variable problem, then go ahead and use the formula. There's nothing wrong with it. I could see you writing a few lines of code in ASM for an embedded device, for instance. In fact, I used this kind of solution in some situations. You don't need to drag large statistical libraries jus... | Why not use the "normal equations" to find simple least squares coefficients? | If you only have to solve this one variable problem, then go ahead and use the formula. There's nothing wrong with it. I could see you writing a few lines of code in ASM for an embedded device, for in | Why not use the "normal equations" to find simple least squares coefficients?
If you only have to solve this one variable problem, then go ahead and use the formula. There's nothing wrong with it. I could see you writing a few lines of code in ASM for an embedded device, for instance. In fact, I used this kind of solut... | Why not use the "normal equations" to find simple least squares coefficients?
If you only have to solve this one variable problem, then go ahead and use the formula. There's nothing wrong with it. I could see you writing a few lines of code in ASM for an embedded device, for in |
15,365 | Why not use the "normal equations" to find simple least squares coefficients? | No modern statistical package would solve a linear regression with the normal equations. The normal equations exist only in the statistical books.
The normal equations shouldn't be used as computing the inverse of matrix is very problematic.
Why use gradient descent for linear regression, when a closed-form math soluti... | Why not use the "normal equations" to find simple least squares coefficients? | No modern statistical package would solve a linear regression with the normal equations. The normal equations exist only in the statistical books.
The normal equations shouldn't be used as computing t | Why not use the "normal equations" to find simple least squares coefficients?
No modern statistical package would solve a linear regression with the normal equations. The normal equations exist only in the statistical books.
The normal equations shouldn't be used as computing the inverse of matrix is very problematic.
... | Why not use the "normal equations" to find simple least squares coefficients?
No modern statistical package would solve a linear regression with the normal equations. The normal equations exist only in the statistical books.
The normal equations shouldn't be used as computing t |
15,366 | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point? | When you have noise in both the dependent variable (vertical errors) and the independent variable (horizontal errors), the least squares objective function can be modified to incorporate these horizontal errors. The problem in how to weight these two types of errors. This weighting usually depends on the ratio of the v... | Why does linear regression use a cost function based on the vertical distance between the hypothesis | When you have noise in both the dependent variable (vertical errors) and the independent variable (horizontal errors), the least squares objective function can be modified to incorporate these horizon | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point?
When you have noise in both the dependent variable (vertical errors) and the independent variable (horizontal errors), the least squares objective function can be modified to incorporate these ... | Why does linear regression use a cost function based on the vertical distance between the hypothesis
When you have noise in both the dependent variable (vertical errors) and the independent variable (horizontal errors), the least squares objective function can be modified to incorporate these horizon |
15,367 | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point? | One reason is that $$\sum_{i=1}^N(y_i-h_\theta(x_i))^2$$ is relatively easy to compute and optimize, while the proposed cost $$\sum_{i=1}^N \min_{x,y}\big[(y_i-h_\theta(x))^2+(x_i-x)^2\big]$$ has a nested minimization problem which may be quite hard depending on the choice of family for $h_\theta(x)$. | Why does linear regression use a cost function based on the vertical distance between the hypothesis | One reason is that $$\sum_{i=1}^N(y_i-h_\theta(x_i))^2$$ is relatively easy to compute and optimize, while the proposed cost $$\sum_{i=1}^N \min_{x,y}\big[(y_i-h_\theta(x))^2+(x_i-x)^2\big]$$ has a ne | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point?
One reason is that $$\sum_{i=1}^N(y_i-h_\theta(x_i))^2$$ is relatively easy to compute and optimize, while the proposed cost $$\sum_{i=1}^N \min_{x,y}\big[(y_i-h_\theta(x))^2+(x_i-x)^2\big]$$ h... | Why does linear regression use a cost function based on the vertical distance between the hypothesis
One reason is that $$\sum_{i=1}^N(y_i-h_\theta(x_i))^2$$ is relatively easy to compute and optimize, while the proposed cost $$\sum_{i=1}^N \min_{x,y}\big[(y_i-h_\theta(x))^2+(x_i-x)^2\big]$$ has a ne |
15,368 | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point? | At the risk of being prosaic, the reason for the error function is that the standard interpretation is that the x is given and one is trying to best describe (or predict) the y component. So there is no error in the 'x'. For example you might try and understand (or predict) the closing price of a stock tomorrow based... | Why does linear regression use a cost function based on the vertical distance between the hypothesis | At the risk of being prosaic, the reason for the error function is that the standard interpretation is that the x is given and one is trying to best describe (or predict) the y component. So there is | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point?
At the risk of being prosaic, the reason for the error function is that the standard interpretation is that the x is given and one is trying to best describe (or predict) the y component. So t... | Why does linear regression use a cost function based on the vertical distance between the hypothesis
At the risk of being prosaic, the reason for the error function is that the standard interpretation is that the x is given and one is trying to best describe (or predict) the y component. So there is |
15,369 | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point? | The oversimplified version is that X is assumed to have no error. So if you look at point E in your plot for example, it's assumed that its X coordinate is precisely accurate. Typically this is the case when we can control X, in other words when we can set it to a specific value. In that case, the only error that ca... | Why does linear regression use a cost function based on the vertical distance between the hypothesis | The oversimplified version is that X is assumed to have no error. So if you look at point E in your plot for example, it's assumed that its X coordinate is precisely accurate. Typically this is the | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point?
The oversimplified version is that X is assumed to have no error. So if you look at point E in your plot for example, it's assumed that its X coordinate is precisely accurate. Typically this ... | Why does linear regression use a cost function based on the vertical distance between the hypothesis
The oversimplified version is that X is assumed to have no error. So if you look at point E in your plot for example, it's assumed that its X coordinate is precisely accurate. Typically this is the |
15,370 | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point? | You are right that, when fitting a line through points, the orthogonal distance is the most natural loss function that can be applied to arbitrary lines (note that the y-distance becomes meaningless for lines perpendicular to the x-axis). This problem is known under a number of names, e.g. "orthogonal regression", or (... | Why does linear regression use a cost function based on the vertical distance between the hypothesis | You are right that, when fitting a line through points, the orthogonal distance is the most natural loss function that can be applied to arbitrary lines (note that the y-distance becomes meaningless f | Why does linear regression use a cost function based on the vertical distance between the hypothesis and the input data point?
You are right that, when fitting a line through points, the orthogonal distance is the most natural loss function that can be applied to arbitrary lines (note that the y-distance becomes meanin... | Why does linear regression use a cost function based on the vertical distance between the hypothesis
You are right that, when fitting a line through points, the orthogonal distance is the most natural loss function that can be applied to arbitrary lines (note that the y-distance becomes meaningless f |
15,371 | How to evaluate the final model after k-fold cross-validation | When training on each fold (90%) of the data, you will then predict on the remaining 10%. With this 10% you will compute an error metric (RMSE, for example). This leaves you with: 10 values for RMSE, and 10 sets of corresponding predictions. There are 2 things to do this these results:
Inspect the mean and standard de... | How to evaluate the final model after k-fold cross-validation | When training on each fold (90%) of the data, you will then predict on the remaining 10%. With this 10% you will compute an error metric (RMSE, for example). This leaves you with: 10 values for RMSE, | How to evaluate the final model after k-fold cross-validation
When training on each fold (90%) of the data, you will then predict on the remaining 10%. With this 10% you will compute an error metric (RMSE, for example). This leaves you with: 10 values for RMSE, and 10 sets of corresponding predictions. There are 2 thin... | How to evaluate the final model after k-fold cross-validation
When training on each fold (90%) of the data, you will then predict on the remaining 10%. With this 10% you will compute an error metric (RMSE, for example). This leaves you with: 10 values for RMSE, |
15,372 | How to evaluate the final model after k-fold cross-validation | Taking the average of the k accuracy scores is a macro-average. Taking the average of the 1000 individual predictions (as described in cavaunpeu's answer) is a micro-average. Both are legitimate and should be broadly similar to each other, so you can use whichever is more convenient - or report both for completeness. | How to evaluate the final model after k-fold cross-validation | Taking the average of the k accuracy scores is a macro-average. Taking the average of the 1000 individual predictions (as described in cavaunpeu's answer) is a micro-average. Both are legitimate and s | How to evaluate the final model after k-fold cross-validation
Taking the average of the k accuracy scores is a macro-average. Taking the average of the 1000 individual predictions (as described in cavaunpeu's answer) is a micro-average. Both are legitimate and should be broadly similar to each other, so you can use whi... | How to evaluate the final model after k-fold cross-validation
Taking the average of the k accuracy scores is a macro-average. Taking the average of the 1000 individual predictions (as described in cavaunpeu's answer) is a micro-average. Both are legitimate and s |
15,373 | Back transformation of an MLR model [duplicate] | This is called the re-transformation problem. I'm going to make your model a little simpler to talk about it:
$\ln{Y} = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_2^2 + \epsilon$
Now, that model does not make predictions for $Y$, it makes predictions for $\ln{Y}$. It is tempting to make predictions for $Y$ by just ... | Back transformation of an MLR model [duplicate] | This is called the re-transformation problem. I'm going to make your model a little simpler to talk about it:
$\ln{Y} = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_2^2 + \epsilon$
Now, that model do | Back transformation of an MLR model [duplicate]
This is called the re-transformation problem. I'm going to make your model a little simpler to talk about it:
$\ln{Y} = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_2^2 + \epsilon$
Now, that model does not make predictions for $Y$, it makes predictions for $\ln{Y}$. It ... | Back transformation of an MLR model [duplicate]
This is called the re-transformation problem. I'm going to make your model a little simpler to talk about it:
$\ln{Y} = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_2^2 + \epsilon$
Now, that model do |
15,374 | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)? | To decide which point forecast error measure to use, we need to take a step back. Note that we don't know the future outcome perfectly, nor will we ever. So the future outcome follows a probability distribution. Some forecasting methods explicitly output such a full distribution, and some don't - but it is always there... | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)? | To decide which point forecast error measure to use, we need to take a step back. Note that we don't know the future outcome perfectly, nor will we ever. So the future outcome follows a probability di | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)?
To decide which point forecast error measure to use, we need to take a step back. Note that we don't know the future outcome perfectly, nor will we ever. So the future outcome follows a probability distribution. Some forecasting me... | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)?
To decide which point forecast error measure to use, we need to take a step back. Note that we don't know the future outcome perfectly, nor will we ever. So the future outcome follows a probability di |
15,375 | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)? | The advantages of using MAE instead of MSE are explained in Davydenko and Fildes (2016), see Section 3.1:
...Some authors (e.g., Zellner, 1986) argue that the criterion by
which we evaluate forecasts should correspond to the criterion by
which we optimise forecasts. In other words, if we optimise estimates
using... | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)? | The advantages of using MAE instead of MSE are explained in Davydenko and Fildes (2016), see Section 3.1:
...Some authors (e.g., Zellner, 1986) argue that the criterion by
which we evaluate forecas | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)?
The advantages of using MAE instead of MSE are explained in Davydenko and Fildes (2016), see Section 3.1:
...Some authors (e.g., Zellner, 1986) argue that the criterion by
which we evaluate forecasts should correspond to the cri... | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)?
The advantages of using MAE instead of MSE are explained in Davydenko and Fildes (2016), see Section 3.1:
...Some authors (e.g., Zellner, 1986) argue that the criterion by
which we evaluate forecas |
15,376 | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)? | Why not compare $RMSE = \sqrt{MSE}$ and $MAE = MAD$?
Actually,
$MAE \leq RMSE \leq \sqrt{n} MAE$ for regression models:
lower bound: each case contributes the same absolute amount of error $e$:
$RMSE = \sqrt{\frac{1}{n} \sum e_i^2} = \sqrt{\frac{1}{n} n e^2} = e = MAE$
upper bound: a single case having error $e$ wh... | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)? | Why not compare $RMSE = \sqrt{MSE}$ and $MAE = MAD$?
Actually,
$MAE \leq RMSE \leq \sqrt{n} MAE$ for regression models:
lower bound: each case contributes the same absolute amount of error $e$:
$R | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)?
Why not compare $RMSE = \sqrt{MSE}$ and $MAE = MAD$?
Actually,
$MAE \leq RMSE \leq \sqrt{n} MAE$ for regression models:
lower bound: each case contributes the same absolute amount of error $e$:
$RMSE = \sqrt{\frac{1}{n} \sum e_... | Why use a certain measure of forecast error (e.g. MAD) as opposed to another (e.g. MSE)?
Why not compare $RMSE = \sqrt{MSE}$ and $MAE = MAD$?
Actually,
$MAE \leq RMSE \leq \sqrt{n} MAE$ for regression models:
lower bound: each case contributes the same absolute amount of error $e$:
$R |
15,377 | Cross validation and parameter optimization | Let us firstly distinguish between two sets of parameters: model parameters (e.g. weights for features in regression), and parameters to the learning algorithm (and hyperparameters). The purpose of cross-validation is to identify learning parameters that generalise well across the population samples we learn from in ea... | Cross validation and parameter optimization | Let us firstly distinguish between two sets of parameters: model parameters (e.g. weights for features in regression), and parameters to the learning algorithm (and hyperparameters). The purpose of cr | Cross validation and parameter optimization
Let us firstly distinguish between two sets of parameters: model parameters (e.g. weights for features in regression), and parameters to the learning algorithm (and hyperparameters). The purpose of cross-validation is to identify learning parameters that generalise well acros... | Cross validation and parameter optimization
Let us firstly distinguish between two sets of parameters: model parameters (e.g. weights for features in regression), and parameters to the learning algorithm (and hyperparameters). The purpose of cr |
15,378 | Cross validation and parameter optimization | I think the currently accepted answer is incomplete in an unfortunate way. I do not agree with the sentence
The purpose of cross-validation is to identify learning parameters
that generalise well across the population samples we learn from in
each fold.
This is indeed one very important application of cross vali... | Cross validation and parameter optimization | I think the currently accepted answer is incomplete in an unfortunate way. I do not agree with the sentence
The purpose of cross-validation is to identify learning parameters
that generalise well | Cross validation and parameter optimization
I think the currently accepted answer is incomplete in an unfortunate way. I do not agree with the sentence
The purpose of cross-validation is to identify learning parameters
that generalise well across the population samples we learn from in
each fold.
This is indeed ... | Cross validation and parameter optimization
I think the currently accepted answer is incomplete in an unfortunate way. I do not agree with the sentence
The purpose of cross-validation is to identify learning parameters
that generalise well |
15,379 | Estimating Markov transition probabilities from sequence data | Please, check the comments above. Here is a quick implementation in R.
x <- c(1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3)
p <- matrix(nrow = 4, ncol = 4, 0)
for (t in 1:(length(x) - 1)) p[x[t], x[t + 1]] <- p[x[t], x[t + 1]] + 1
for (i in 1:4) p[i, ] <- p[i, ] / sum(p[i, ])
Results:
> p
[,1] ... | Estimating Markov transition probabilities from sequence data | Please, check the comments above. Here is a quick implementation in R.
x <- c(1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3)
p <- matrix(nrow = 4, ncol = 4, 0)
for (t in 1:(length(x) - 1)) p[x | Estimating Markov transition probabilities from sequence data
Please, check the comments above. Here is a quick implementation in R.
x <- c(1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3)
p <- matrix(nrow = 4, ncol = 4, 0)
for (t in 1:(length(x) - 1)) p[x[t], x[t + 1]] <- p[x[t], x[t + 1]] + 1
for (i in 1:4) p[i... | Estimating Markov transition probabilities from sequence data
Please, check the comments above. Here is a quick implementation in R.
x <- c(1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3)
p <- matrix(nrow = 4, ncol = 4, 0)
for (t in 1:(length(x) - 1)) p[x |
15,380 | Estimating Markov transition probabilities from sequence data | Here is my implementation in R
x <- c(1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3)
xChar<-as.character(x)
library(markovchain)
mcX<-markovchainFit(xChar)$estimate
mcX | Estimating Markov transition probabilities from sequence data | Here is my implementation in R
x <- c(1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3)
xChar<-as.character(x)
library(markovchain)
mcX<-markovchainFit(xChar)$estimate
mcX | Estimating Markov transition probabilities from sequence data
Here is my implementation in R
x <- c(1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3)
xChar<-as.character(x)
library(markovchain)
mcX<-markovchainFit(xChar)$estimate
mcX | Estimating Markov transition probabilities from sequence data
Here is my implementation in R
x <- c(1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3)
xChar<-as.character(x)
library(markovchain)
mcX<-markovchainFit(xChar)$estimate
mcX |
15,381 | Estimating Markov transition probabilities from sequence data | Here is a way to do it in Matlab:
x = [1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3];
counts_mat = full(sparse(x(1:end-1),x(2:end),1));
trans_mat = bsxfun(@rdivide,counts_mat,sum(counts_mat,2))
Acknowledgement owed to SomptingGuy: http://www.eng-tips.com/viewthread.cfm?qid=236532 | Estimating Markov transition probabilities from sequence data | Here is a way to do it in Matlab:
x = [1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3];
counts_mat = full(sparse(x(1:end-1),x(2:end),1));
trans_mat = bsxfun(@rdivide,counts_mat,sum(counts_mat,2 | Estimating Markov transition probabilities from sequence data
Here is a way to do it in Matlab:
x = [1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3];
counts_mat = full(sparse(x(1:end-1),x(2:end),1));
trans_mat = bsxfun(@rdivide,counts_mat,sum(counts_mat,2))
Acknowledgement owed to SomptingGuy: http://www.eng-ti... | Estimating Markov transition probabilities from sequence data
Here is a way to do it in Matlab:
x = [1,2,1,1,3,4,4,1,2,4,1,4,3,4,4,4,3,1,3,2,3,3,3,4,2,2,3];
counts_mat = full(sparse(x(1:end-1),x(2:end),1));
trans_mat = bsxfun(@rdivide,counts_mat,sum(counts_mat,2 |
15,382 | Calculating the 95th percentile: Comparing normal distribution, R Quantile, and Excel approaches | The first approach is completely wrong and has nothing to do with the 95th percentile, in my opinion.
The second approach seems to be based on an assumption that the data is normally distributed, but it should be about 1.645 standard deviations above the mean, not 2 standard deviations, and it looks like you realised t... | Calculating the 95th percentile: Comparing normal distribution, R Quantile, and Excel approaches | The first approach is completely wrong and has nothing to do with the 95th percentile, in my opinion.
The second approach seems to be based on an assumption that the data is normally distributed, but | Calculating the 95th percentile: Comparing normal distribution, R Quantile, and Excel approaches
The first approach is completely wrong and has nothing to do with the 95th percentile, in my opinion.
The second approach seems to be based on an assumption that the data is normally distributed, but it should be about 1.64... | Calculating the 95th percentile: Comparing normal distribution, R Quantile, and Excel approaches
The first approach is completely wrong and has nothing to do with the 95th percentile, in my opinion.
The second approach seems to be based on an assumption that the data is normally distributed, but |
15,383 | Calculating the 95th percentile: Comparing normal distribution, R Quantile, and Excel approaches | Here are a few points to supplement @mark999's answer.
Wikipedia has an article on percentiles where it is noted that no standard definition of a percentile exists. However, several formulas are discussed.
Crawford, J.; Garthwaite, P. & Slick, D. On percentile norms in neuropsychology: Proposed reporting standards and... | Calculating the 95th percentile: Comparing normal distribution, R Quantile, and Excel approaches | Here are a few points to supplement @mark999's answer.
Wikipedia has an article on percentiles where it is noted that no standard definition of a percentile exists. However, several formulas are disc | Calculating the 95th percentile: Comparing normal distribution, R Quantile, and Excel approaches
Here are a few points to supplement @mark999's answer.
Wikipedia has an article on percentiles where it is noted that no standard definition of a percentile exists. However, several formulas are discussed.
Crawford, J.; Ga... | Calculating the 95th percentile: Comparing normal distribution, R Quantile, and Excel approaches
Here are a few points to supplement @mark999's answer.
Wikipedia has an article on percentiles where it is noted that no standard definition of a percentile exists. However, several formulas are disc |
15,384 | What is the difference between the "coef" and "(exp)coef" output of coxph in R? | If you have a single explanatory variable, say treatment group, a Cox's regression model is fitted with coxph(); the coefficient (coef) reads as a regression coefficient (in the context of the Cox model, described hereafter) and its exponential gives you the hazard in the treatment group (compared to the control or pla... | What is the difference between the "coef" and "(exp)coef" output of coxph in R? | If you have a single explanatory variable, say treatment group, a Cox's regression model is fitted with coxph(); the coefficient (coef) reads as a regression coefficient (in the context of the Cox mod | What is the difference between the "coef" and "(exp)coef" output of coxph in R?
If you have a single explanatory variable, say treatment group, a Cox's regression model is fitted with coxph(); the coefficient (coef) reads as a regression coefficient (in the context of the Cox model, described hereafter) and its exponen... | What is the difference between the "coef" and "(exp)coef" output of coxph in R?
If you have a single explanatory variable, say treatment group, a Cox's regression model is fitted with coxph(); the coefficient (coef) reads as a regression coefficient (in the context of the Cox mod |
15,385 | What is the difference between the "coef" and "(exp)coef" output of coxph in R? | To quote the documentation for the print method for a coxph object, obtained in R by typing ?survival::print.coxph:
coefficients the coefficients of the linear predictor, which multiply the columns of the model matrix.
That's all the documentation the author of the package provides. The package contains no user gu... | What is the difference between the "coef" and "(exp)coef" output of coxph in R? | To quote the documentation for the print method for a coxph object, obtained in R by typing ?survival::print.coxph:
coefficients the coefficients of the linear predictor, which multiply the column | What is the difference between the "coef" and "(exp)coef" output of coxph in R?
To quote the documentation for the print method for a coxph object, obtained in R by typing ?survival::print.coxph:
coefficients the coefficients of the linear predictor, which multiply the columns of the model matrix.
That's all the d... | What is the difference between the "coef" and "(exp)coef" output of coxph in R?
To quote the documentation for the print method for a coxph object, obtained in R by typing ?survival::print.coxph:
coefficients the coefficients of the linear predictor, which multiply the column |
15,386 | Poisson regression with large data: is it wrong to change the unit of measurement? | When you're dealing with a Poisson distribution with large values of \lambda (its parameter), it is common to use a normal approximation to the Poisson distribution.
As this site mentions, it's all right to use the normal approximation when \lambda gets over 20, and the approximation improves as \lambda gets even high... | Poisson regression with large data: is it wrong to change the unit of measurement? | When you're dealing with a Poisson distribution with large values of \lambda (its parameter), it is common to use a normal approximation to the Poisson distribution.
As this site mentions, it's all r | Poisson regression with large data: is it wrong to change the unit of measurement?
When you're dealing with a Poisson distribution with large values of \lambda (its parameter), it is common to use a normal approximation to the Poisson distribution.
As this site mentions, it's all right to use the normal approximation ... | Poisson regression with large data: is it wrong to change the unit of measurement?
When you're dealing with a Poisson distribution with large values of \lambda (its parameter), it is common to use a normal approximation to the Poisson distribution.
As this site mentions, it's all r |
15,387 | Poisson regression with large data: is it wrong to change the unit of measurement? | In case of Poisson it is bad, since counts are counts -- their unit is an unity. On the other hand, if you'd use some advanced software like R, its Poisson handling functions will be aware of such large numbers and would use some numerical tricks to handle them.
Obviously I agree that normal approximation is another go... | Poisson regression with large data: is it wrong to change the unit of measurement? | In case of Poisson it is bad, since counts are counts -- their unit is an unity. On the other hand, if you'd use some advanced software like R, its Poisson handling functions will be aware of such lar | Poisson regression with large data: is it wrong to change the unit of measurement?
In case of Poisson it is bad, since counts are counts -- their unit is an unity. On the other hand, if you'd use some advanced software like R, its Poisson handling functions will be aware of such large numbers and would use some numeric... | Poisson regression with large data: is it wrong to change the unit of measurement?
In case of Poisson it is bad, since counts are counts -- their unit is an unity. On the other hand, if you'd use some advanced software like R, its Poisson handling functions will be aware of such lar |
15,388 | Poisson regression with large data: is it wrong to change the unit of measurement? | Most statistical packages have a function to calculate the natural logarithm of the factorial directly (e.g. the lfactorial() function in R, the lnfactorial() function in Stata). This allows you to include the constant term in the log-likelihood if you want. | Poisson regression with large data: is it wrong to change the unit of measurement? | Most statistical packages have a function to calculate the natural logarithm of the factorial directly (e.g. the lfactorial() function in R, the lnfactorial() function in Stata). This allows you to in | Poisson regression with large data: is it wrong to change the unit of measurement?
Most statistical packages have a function to calculate the natural logarithm of the factorial directly (e.g. the lfactorial() function in R, the lnfactorial() function in Stata). This allows you to include the constant term in the log-li... | Poisson regression with large data: is it wrong to change the unit of measurement?
Most statistical packages have a function to calculate the natural logarithm of the factorial directly (e.g. the lfactorial() function in R, the lnfactorial() function in Stata). This allows you to in |
15,389 | Poisson regression with large data: is it wrong to change the unit of measurement? | I'm afraid you can't do that. As @Baltimark states, with big lambda the distribution will be of more normal shape (symmetric), and with scaling it down it will no longer be poisson distrubution. Try the following code in R:
poi1 = rpois(100000, lambda = 5) # poisson
poi2 = rpois(100000, lambda = 100)/20 # scaled-down ... | Poisson regression with large data: is it wrong to change the unit of measurement? | I'm afraid you can't do that. As @Baltimark states, with big lambda the distribution will be of more normal shape (symmetric), and with scaling it down it will no longer be poisson distrubution. Try t | Poisson regression with large data: is it wrong to change the unit of measurement?
I'm afraid you can't do that. As @Baltimark states, with big lambda the distribution will be of more normal shape (symmetric), and with scaling it down it will no longer be poisson distrubution. Try the following code in R:
poi1 = rpois(... | Poisson regression with large data: is it wrong to change the unit of measurement?
I'm afraid you can't do that. As @Baltimark states, with big lambda the distribution will be of more normal shape (symmetric), and with scaling it down it will no longer be poisson distrubution. Try t |
15,390 | Poisson regression with large data: is it wrong to change the unit of measurement? | You can simply ignore the 'factorial' when using maximum likelihood. Here is the reasoning for your suicides example. Let:
λ : Be the expected number of suicides per year
ki: Be the number of suicides in year i.
Then you would maximize the log-likelihood as:
LL = ∑ ( ki log(λ) - λ - ki! )
Maximizing the above is equiva... | Poisson regression with large data: is it wrong to change the unit of measurement? | You can simply ignore the 'factorial' when using maximum likelihood. Here is the reasoning for your suicides example. Let:
λ : Be the expected number of suicides per year
ki: Be the number of suicides | Poisson regression with large data: is it wrong to change the unit of measurement?
You can simply ignore the 'factorial' when using maximum likelihood. Here is the reasoning for your suicides example. Let:
λ : Be the expected number of suicides per year
ki: Be the number of suicides in year i.
Then you would maximize t... | Poisson regression with large data: is it wrong to change the unit of measurement?
You can simply ignore the 'factorial' when using maximum likelihood. Here is the reasoning for your suicides example. Let:
λ : Be the expected number of suicides per year
ki: Be the number of suicides |
15,391 | Why pure exponent is not used as activation function for neural networks? | I think the most prominent reason is stability. Think about having consequent layers with exponential activation, and what happens to the output when you input a small number to the NN (e.g. $x=1$), the forward calculation will look like:
$$o=\exp(\exp(\exp(\exp(1))))\approx e^{3814279}$$
It can go crazy very quickly a... | Why pure exponent is not used as activation function for neural networks? | I think the most prominent reason is stability. Think about having consequent layers with exponential activation, and what happens to the output when you input a small number to the NN (e.g. $x=1$), t | Why pure exponent is not used as activation function for neural networks?
I think the most prominent reason is stability. Think about having consequent layers with exponential activation, and what happens to the output when you input a small number to the NN (e.g. $x=1$), the forward calculation will look like:
$$o=\ex... | Why pure exponent is not used as activation function for neural networks?
I think the most prominent reason is stability. Think about having consequent layers with exponential activation, and what happens to the output when you input a small number to the NN (e.g. $x=1$), t |
15,392 | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate | This is an extremely interesting problem. I reviewed your code and can find no immediately obvious typo.
I would like to see you redo this simulation but use the maximum likelihood test to make inference about the heterogeneity between groups. This would involve refitting a null model so that you can get estimates of t... | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate | This is an extremely interesting problem. I reviewed your code and can find no immediately obvious typo.
I would like to see you redo this simulation but use the maximum likelihood test to make infere | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate
This is an extremely interesting problem. I reviewed your code and can find no immediately obvious typo.
I would like to see you redo this simulation but use the maximum likelihood test to make inference about the heterogeneity betwe... | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate
This is an extremely interesting problem. I reviewed your code and can find no immediately obvious typo.
I would like to see you redo this simulation but use the maximum likelihood test to make infere |
15,393 | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate | The O'Hara and Kotze paper (Methods in Ecology and Evolution 1:118–122) is not a good starting point for discussion. My most serious concern is the claim in point 4 of the summary:
We found that the transformations performed poorly, except . . .. The
quasi-Poisson and negative binomial models ... [showed] little b... | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate | The O'Hara and Kotze paper (Methods in Ecology and Evolution 1:118–122) is not a good starting point for discussion. My most serious concern is the claim in point 4 of the summary:
We found that the | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate
The O'Hara and Kotze paper (Methods in Ecology and Evolution 1:118–122) is not a good starting point for discussion. My most serious concern is the claim in point 4 of the summary:
We found that the transformations performed poorly... | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate
The O'Hara and Kotze paper (Methods in Ecology and Evolution 1:118–122) is not a good starting point for discussion. My most serious concern is the claim in point 4 of the summary:
We found that the |
15,394 | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate | The original post reflects Tony Ives' paper: Ives (2015). It's clear that significance testing gives different results to parameter estimation.
John Maindonald explains why the estimates are biased, but his ignorance of the background is annoying - he criticises us for showing that a method we all agree is flawed is fl... | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate | The original post reflects Tony Ives' paper: Ives (2015). It's clear that significance testing gives different results to parameter estimation.
John Maindonald explains why the estimates are biased, b | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate
The original post reflects Tony Ives' paper: Ives (2015). It's clear that significance testing gives different results to parameter estimation.
John Maindonald explains why the estimates are biased, but his ignorance of the backgroun... | Negative-binomial GLM vs. log-transforming for count data: increased Type I error rate
The original post reflects Tony Ives' paper: Ives (2015). It's clear that significance testing gives different results to parameter estimation.
John Maindonald explains why the estimates are biased, b |
15,395 | Strategy for editing comma separated value (CSV) files | If you are comfortable with R, you can create your basic data.frame and then use the fix() function on it to input data. Along the same line as #5, once you set up the data.frame you can use a series of readLines(n=1) (or whatever) to get your data in, validate it, and the provide the opportunity to add the next row. ... | Strategy for editing comma separated value (CSV) files | If you are comfortable with R, you can create your basic data.frame and then use the fix() function on it to input data. Along the same line as #5, once you set up the data.frame you can use a series | Strategy for editing comma separated value (CSV) files
If you are comfortable with R, you can create your basic data.frame and then use the fix() function on it to input data. Along the same line as #5, once you set up the data.frame you can use a series of readLines(n=1) (or whatever) to get your data in, validate it... | Strategy for editing comma separated value (CSV) files
If you are comfortable with R, you can create your basic data.frame and then use the fix() function on it to input data. Along the same line as #5, once you set up the data.frame you can use a series |
15,396 | Strategy for editing comma separated value (CSV) files | Update: [Having been going through a large backlog of email from R-Help] I am reminded of the thread on "The behaviour of read.csv()". In this, Duncan Murdoch mentions that he prefers to use Data Interchange Format (DIF) files instead of csv for some of the reason Jeromy mentions. I just tried this and Gnumeric gets it... | Strategy for editing comma separated value (CSV) files | Update: [Having been going through a large backlog of email from R-Help] I am reminded of the thread on "The behaviour of read.csv()". In this, Duncan Murdoch mentions that he prefers to use Data Inte | Strategy for editing comma separated value (CSV) files
Update: [Having been going through a large backlog of email from R-Help] I am reminded of the thread on "The behaviour of read.csv()". In this, Duncan Murdoch mentions that he prefers to use Data Interchange Format (DIF) files instead of csv for some of the reason ... | Strategy for editing comma separated value (CSV) files
Update: [Having been going through a large backlog of email from R-Help] I am reminded of the thread on "The behaviour of read.csv()". In this, Duncan Murdoch mentions that he prefers to use Data Inte |
15,397 | Strategy for editing comma separated value (CSV) files | I suggest you look at google refine (http://code.google.com/p/google-refine/). I think is a very good tool for editing CSV files | Strategy for editing comma separated value (CSV) files | I suggest you look at google refine (http://code.google.com/p/google-refine/). I think is a very good tool for editing CSV files | Strategy for editing comma separated value (CSV) files
I suggest you look at google refine (http://code.google.com/p/google-refine/). I think is a very good tool for editing CSV files | Strategy for editing comma separated value (CSV) files
I suggest you look at google refine (http://code.google.com/p/google-refine/). I think is a very good tool for editing CSV files |
15,398 | Strategy for editing comma separated value (CSV) files | I would avoid working with the CSV and TSV files all together. Instead learn to use SQL and operate only on a datamart or database (DB) copy of your data or you can use SAS or R with a passthru connection to your database. That way you can make bulk updates to your data instead of doing the dreaded find and replace i... | Strategy for editing comma separated value (CSV) files | I would avoid working with the CSV and TSV files all together. Instead learn to use SQL and operate only on a datamart or database (DB) copy of your data or you can use SAS or R with a passthru conne | Strategy for editing comma separated value (CSV) files
I would avoid working with the CSV and TSV files all together. Instead learn to use SQL and operate only on a datamart or database (DB) copy of your data or you can use SAS or R with a passthru connection to your database. That way you can make bulk updates to yo... | Strategy for editing comma separated value (CSV) files
I would avoid working with the CSV and TSV files all together. Instead learn to use SQL and operate only on a datamart or database (DB) copy of your data or you can use SAS or R with a passthru conne |
15,399 | Strategy for editing comma separated value (CSV) files | After I asked this question, I started having a look at CSVed.
From the website:
CSVed is an easy and powerful CSV file
editor, you can manipulate any CSV
file, separated with any separator.
I'm not sure if anyone has experience with it. | Strategy for editing comma separated value (CSV) files | After I asked this question, I started having a look at CSVed.
From the website:
CSVed is an easy and powerful CSV file
editor, you can manipulate any CSV
file, separated with any separator.
I'm | Strategy for editing comma separated value (CSV) files
After I asked this question, I started having a look at CSVed.
From the website:
CSVed is an easy and powerful CSV file
editor, you can manipulate any CSV
file, separated with any separator.
I'm not sure if anyone has experience with it. | Strategy for editing comma separated value (CSV) files
After I asked this question, I started having a look at CSVed.
From the website:
CSVed is an easy and powerful CSV file
editor, you can manipulate any CSV
file, separated with any separator.
I'm |
15,400 | Strategy for editing comma separated value (CSV) files | Excel is not very CSV friendly. For example, if you were to enter "1,300" into Excel, and save that as a comma separated value, it would let you! This can be a big problem (I encounter it on a regular basis when receiving files from others).
I personally use OpenOffice.org Calc, I also use many of the solutions listed ... | Strategy for editing comma separated value (CSV) files | Excel is not very CSV friendly. For example, if you were to enter "1,300" into Excel, and save that as a comma separated value, it would let you! This can be a big problem (I encounter it on a regular | Strategy for editing comma separated value (CSV) files
Excel is not very CSV friendly. For example, if you were to enter "1,300" into Excel, and save that as a comma separated value, it would let you! This can be a big problem (I encounter it on a regular basis when receiving files from others).
I personally use OpenOf... | Strategy for editing comma separated value (CSV) files
Excel is not very CSV friendly. For example, if you were to enter "1,300" into Excel, and save that as a comma separated value, it would let you! This can be a big problem (I encounter it on a regular |
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