idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k β | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 β | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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8,601 | XGBoost vs Python Sklearn gradient boosted trees | Unlike the Sklearn's gradient boosting, Xgboost does regularization of the tree as well to avoid overfitting and it deals with the missing values efficiently as well. Following link might be helpful to learn xgboost precisely https://www.youtube.com/watch?v=Vly8xGnNiWs | XGBoost vs Python Sklearn gradient boosted trees | Unlike the Sklearn's gradient boosting, Xgboost does regularization of the tree as well to avoid overfitting and it deals with the missing values efficiently as well. Following link might be helpful t | XGBoost vs Python Sklearn gradient boosted trees
Unlike the Sklearn's gradient boosting, Xgboost does regularization of the tree as well to avoid overfitting and it deals with the missing values efficiently as well. Following link might be helpful to learn xgboost precisely https://www.youtube.com/watch?v=Vly8xGnNiWs | XGBoost vs Python Sklearn gradient boosted trees
Unlike the Sklearn's gradient boosting, Xgboost does regularization of the tree as well to avoid overfitting and it deals with the missing values efficiently as well. Following link might be helpful t |
8,602 | XGBoost vs Python Sklearn gradient boosted trees | XGboost is implementation of GBDT with randmization(It uses coloumn sampling and row sampling).Row sampling is possible by not using all of the training data for each base model of the GBDT. Instead of using all of the training data for each base-model, we sample a subset of rows and use only those rows of data to buil... | XGBoost vs Python Sklearn gradient boosted trees | XGboost is implementation of GBDT with randmization(It uses coloumn sampling and row sampling).Row sampling is possible by not using all of the training data for each base model of the GBDT. Instead o | XGBoost vs Python Sklearn gradient boosted trees
XGboost is implementation of GBDT with randmization(It uses coloumn sampling and row sampling).Row sampling is possible by not using all of the training data for each base model of the GBDT. Instead of using all of the training data for each base-model, we sample a subse... | XGBoost vs Python Sklearn gradient boosted trees
XGboost is implementation of GBDT with randmization(It uses coloumn sampling and row sampling).Row sampling is possible by not using all of the training data for each base model of the GBDT. Instead o |
8,603 | How to deal with multicollinearity when performing variable selection? | First off, a very good resource for this problem is T. Keith, Multiple Regression and Beyond. There is a lot of material in the book about path modeling and variables selection and I think you will find exhaustive answers to your questions there.
One way to address multicollinearity is to center the predictors, that is... | How to deal with multicollinearity when performing variable selection? | First off, a very good resource for this problem is T. Keith, Multiple Regression and Beyond. There is a lot of material in the book about path modeling and variables selection and I think you will fi | How to deal with multicollinearity when performing variable selection?
First off, a very good resource for this problem is T. Keith, Multiple Regression and Beyond. There is a lot of material in the book about path modeling and variables selection and I think you will find exhaustive answers to your questions there.
On... | How to deal with multicollinearity when performing variable selection?
First off, a very good resource for this problem is T. Keith, Multiple Regression and Beyond. There is a lot of material in the book about path modeling and variables selection and I think you will fi |
8,604 | How to deal with multicollinearity when performing variable selection? | Because it is so hard to determine which variables to drop, it is often better not to drop variables. Two ways to proceed along this line are (1) use a data reduction method (e.g., variable clustering or principal components) and put summary scores into the model instead of individual variables and (2) put all variabl... | How to deal with multicollinearity when performing variable selection? | Because it is so hard to determine which variables to drop, it is often better not to drop variables. Two ways to proceed along this line are (1) use a data reduction method (e.g., variable clusterin | How to deal with multicollinearity when performing variable selection?
Because it is so hard to determine which variables to drop, it is often better not to drop variables. Two ways to proceed along this line are (1) use a data reduction method (e.g., variable clustering or principal components) and put summary scores... | How to deal with multicollinearity when performing variable selection?
Because it is so hard to determine which variables to drop, it is often better not to drop variables. Two ways to proceed along this line are (1) use a data reduction method (e.g., variable clusterin |
8,605 | How to deal with multicollinearity when performing variable selection? | If you would like to carry out variable selection in the presence of high collinearity I can recommend the l0ara package, which fits L0 penalized GLMs using an iterative adaptive ridge procedure. As this method is ultimately based on ridge regularized regression, it can deal very well with collinearity, and in my simul... | How to deal with multicollinearity when performing variable selection? | If you would like to carry out variable selection in the presence of high collinearity I can recommend the l0ara package, which fits L0 penalized GLMs using an iterative adaptive ridge procedure. As t | How to deal with multicollinearity when performing variable selection?
If you would like to carry out variable selection in the presence of high collinearity I can recommend the l0ara package, which fits L0 penalized GLMs using an iterative adaptive ridge procedure. As this method is ultimately based on ridge regulariz... | How to deal with multicollinearity when performing variable selection?
If you would like to carry out variable selection in the presence of high collinearity I can recommend the l0ara package, which fits L0 penalized GLMs using an iterative adaptive ridge procedure. As t |
8,606 | Is interaction possible between two continuous variables? | Yes, why not? The same consideration as for categorical variables would apply in this case: The effect of $X_1$ on the outcome $Y$ is not the same depending on the value of $X_2$. To help visualize it, you can think of the values taken by $X_1$ when $X_2$ takes high or low values. Contrary to categorical variables, her... | Is interaction possible between two continuous variables? | Yes, why not? The same consideration as for categorical variables would apply in this case: The effect of $X_1$ on the outcome $Y$ is not the same depending on the value of $X_2$. To help visualize it | Is interaction possible between two continuous variables?
Yes, why not? The same consideration as for categorical variables would apply in this case: The effect of $X_1$ on the outcome $Y$ is not the same depending on the value of $X_2$. To help visualize it, you can think of the values taken by $X_1$ when $X_2$ takes ... | Is interaction possible between two continuous variables?
Yes, why not? The same consideration as for categorical variables would apply in this case: The effect of $X_1$ on the outcome $Y$ is not the same depending on the value of $X_2$. To help visualize it |
8,607 | Is rejecting the hypothesis using p-value equivalent to hypothesis not belonging to the confidence interval? | Yes and no.
First the "yes"
What you've observed is that when a test and a confidence interval is based on the same statistic, there is an equivalence between them: we can interpret the $p$-value as the smallest value of $\alpha$ for which the null value of the parameter would be included in the $1-\alpha$ confidence i... | Is rejecting the hypothesis using p-value equivalent to hypothesis not belonging to the confidence i | Yes and no.
First the "yes"
What you've observed is that when a test and a confidence interval is based on the same statistic, there is an equivalence between them: we can interpret the $p$-value as t | Is rejecting the hypothesis using p-value equivalent to hypothesis not belonging to the confidence interval?
Yes and no.
First the "yes"
What you've observed is that when a test and a confidence interval is based on the same statistic, there is an equivalence between them: we can interpret the $p$-value as the smallest... | Is rejecting the hypothesis using p-value equivalent to hypothesis not belonging to the confidence i
Yes and no.
First the "yes"
What you've observed is that when a test and a confidence interval is based on the same statistic, there is an equivalence between them: we can interpret the $p$-value as t |
8,608 | Is rejecting the hypothesis using p-value equivalent to hypothesis not belonging to the confidence interval? | When looking at a single parameter, it is possible that a test about the value of the parameter and the confidence interval "mismatch" depending on how they are constructed. In particular, a hypothesis test is a level $\alpha$-test, if it rejects the null hypothesis a proportion $\leq \alpha$ of the time when the null ... | Is rejecting the hypothesis using p-value equivalent to hypothesis not belonging to the confidence i | When looking at a single parameter, it is possible that a test about the value of the parameter and the confidence interval "mismatch" depending on how they are constructed. In particular, a hypothesi | Is rejecting the hypothesis using p-value equivalent to hypothesis not belonging to the confidence interval?
When looking at a single parameter, it is possible that a test about the value of the parameter and the confidence interval "mismatch" depending on how they are constructed. In particular, a hypothesis test is a... | Is rejecting the hypothesis using p-value equivalent to hypothesis not belonging to the confidence i
When looking at a single parameter, it is possible that a test about the value of the parameter and the confidence interval "mismatch" depending on how they are constructed. In particular, a hypothesi |
8,609 | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients | In short, I wouldn't use both the partial $R^2$ and the standardized coefficients in the same analysis, as they are not independent. I would argue that it is usually probably more intuitive to compare relationships using the standardized coefficients because they relate readily to the model definition (i.e. $Y = \beta ... | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients | In short, I wouldn't use both the partial $R^2$ and the standardized coefficients in the same analysis, as they are not independent. I would argue that it is usually probably more intuitive to compare | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
In short, I wouldn't use both the partial $R^2$ and the standardized coefficients in the same analysis, as they are not independent. I would argue that it is usually probably more intuitive to compare relationships using the st... | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
In short, I wouldn't use both the partial $R^2$ and the standardized coefficients in the same analysis, as they are not independent. I would argue that it is usually probably more intuitive to compare |
8,610 | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients | As already explained in several other answers and in comments, this question was based on at least three confusions:
Function anova() uses sequential (also called type I) sum of squares (SS) decomposition that depends on the order of predictors. A decomposition corresponding to the regression coefficients and $t$-test... | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients | As already explained in several other answers and in comments, this question was based on at least three confusions:
Function anova() uses sequential (also called type I) sum of squares (SS) decompos | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
As already explained in several other answers and in comments, this question was based on at least three confusions:
Function anova() uses sequential (also called type I) sum of squares (SS) decomposition that depends on the o... | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
As already explained in several other answers and in comments, this question was based on at least three confusions:
Function anova() uses sequential (also called type I) sum of squares (SS) decompos |
8,611 | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients | You wrote:
My question is: Should I use partial RΒ² or the coefficients to show how much influence each factor has on the outcome?
It is important not to confuse two things here. First, there is the question of model specification. The lm algorithm assumes that the OLS-assumptions are met. Among other things this me... | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients | You wrote:
My question is: Should I use partial RΒ² or the coefficients to show how much influence each factor has on the outcome?
It is important not to confuse two things here. First, there is th | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
You wrote:
My question is: Should I use partial RΒ² or the coefficients to show how much influence each factor has on the outcome?
It is important not to confuse two things here. First, there is the question of model specifi... | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
You wrote:
My question is: Should I use partial RΒ² or the coefficients to show how much influence each factor has on the outcome?
It is important not to confuse two things here. First, there is th |
8,612 | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients | Regarding the difference between the linear regression coefficient and the partial correlation you may read this, for example.
However, the confusion expressed in the question seems to be of another nature. It appears to be about the default type of sums-of-squares used by this or that statistical package (topic, repea... | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients | Regarding the difference between the linear regression coefficient and the partial correlation you may read this, for example.
However, the confusion expressed in the question seems to be of another n | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
Regarding the difference between the linear regression coefficient and the partial correlation you may read this, for example.
However, the confusion expressed in the question seems to be of another nature. It appears to be abo... | Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
Regarding the difference between the linear regression coefficient and the partial correlation you may read this, for example.
However, the confusion expressed in the question seems to be of another n |
8,613 | Is unbiased maximum likelihood estimator always the best unbiased estimator? | But generally, if we have an unbiased MLE, would it also be the best
unbiased estimator ?
If there is a complete sufficient statistics, yes.
Proof:
LehmannβScheffΓ© theorem: Any unbiased estimator that is a function of a complete sufficient statistics
is the best (UMVUE).
MLE is a function of any sufficient statis... | Is unbiased maximum likelihood estimator always the best unbiased estimator? | But generally, if we have an unbiased MLE, would it also be the best
unbiased estimator ?
If there is a complete sufficient statistics, yes.
Proof:
LehmannβScheffΓ© theorem: Any unbiased estimator | Is unbiased maximum likelihood estimator always the best unbiased estimator?
But generally, if we have an unbiased MLE, would it also be the best
unbiased estimator ?
If there is a complete sufficient statistics, yes.
Proof:
LehmannβScheffΓ© theorem: Any unbiased estimator that is a function of a complete sufficien... | Is unbiased maximum likelihood estimator always the best unbiased estimator?
But generally, if we have an unbiased MLE, would it also be the best
unbiased estimator ?
If there is a complete sufficient statistics, yes.
Proof:
LehmannβScheffΓ© theorem: Any unbiased estimator |
8,614 | Is unbiased maximum likelihood estimator always the best unbiased estimator? | In my opinion, the question is not truly coherent in that the maximisation of a likelihood and unbiasedness do not get along, if only because maximum likelihood estimators are equivariant, ie the transform of the estimator is the estimator of the transform of the parameter, while unbiasedness does not stand under non-l... | Is unbiased maximum likelihood estimator always the best unbiased estimator? | In my opinion, the question is not truly coherent in that the maximisation of a likelihood and unbiasedness do not get along, if only because maximum likelihood estimators are equivariant, ie the tran | Is unbiased maximum likelihood estimator always the best unbiased estimator?
In my opinion, the question is not truly coherent in that the maximisation of a likelihood and unbiasedness do not get along, if only because maximum likelihood estimators are equivariant, ie the transform of the estimator is the estimator of ... | Is unbiased maximum likelihood estimator always the best unbiased estimator?
In my opinion, the question is not truly coherent in that the maximisation of a likelihood and unbiasedness do not get along, if only because maximum likelihood estimators are equivariant, ie the tran |
8,615 | Is unbiased maximum likelihood estimator always the best unbiased estimator? | MLE's asymptotic variance is UMVUE i.e attains cramer rao lower bound but finite variance may not be UMVUE to make sure that estimator is UMVUE it should be sufficient and complete statistics or any function of that statistics. | Is unbiased maximum likelihood estimator always the best unbiased estimator? | MLE's asymptotic variance is UMVUE i.e attains cramer rao lower bound but finite variance may not be UMVUE to make sure that estimator is UMVUE it should be sufficient and complete statistics or any f | Is unbiased maximum likelihood estimator always the best unbiased estimator?
MLE's asymptotic variance is UMVUE i.e attains cramer rao lower bound but finite variance may not be UMVUE to make sure that estimator is UMVUE it should be sufficient and complete statistics or any function of that statistics. | Is unbiased maximum likelihood estimator always the best unbiased estimator?
MLE's asymptotic variance is UMVUE i.e attains cramer rao lower bound but finite variance may not be UMVUE to make sure that estimator is UMVUE it should be sufficient and complete statistics or any f |
8,616 | Is unbiased maximum likelihood estimator always the best unbiased estimator? | In short, an estimator is UMVUE, if it is unbiased and the function of a complete and sufficient statistic. (See Rao-Blackwell and Scheffe) | Is unbiased maximum likelihood estimator always the best unbiased estimator? | In short, an estimator is UMVUE, if it is unbiased and the function of a complete and sufficient statistic. (See Rao-Blackwell and Scheffe) | Is unbiased maximum likelihood estimator always the best unbiased estimator?
In short, an estimator is UMVUE, if it is unbiased and the function of a complete and sufficient statistic. (See Rao-Blackwell and Scheffe) | Is unbiased maximum likelihood estimator always the best unbiased estimator?
In short, an estimator is UMVUE, if it is unbiased and the function of a complete and sufficient statistic. (See Rao-Blackwell and Scheffe) |
8,617 | R caret and NAs | To the train function in caret, you can pass the parameter na.action = na.pass, and no preprocessing (do not specify preProcess, leave it as its default value NULL). This will pass the NA values unmodified directly to the prediction function (this will cause prediction functions that do not support missing values to fa... | R caret and NAs | To the train function in caret, you can pass the parameter na.action = na.pass, and no preprocessing (do not specify preProcess, leave it as its default value NULL). This will pass the NA values unmod | R caret and NAs
To the train function in caret, you can pass the parameter na.action = na.pass, and no preprocessing (do not specify preProcess, leave it as its default value NULL). This will pass the NA values unmodified directly to the prediction function (this will cause prediction functions that do not support miss... | R caret and NAs
To the train function in caret, you can pass the parameter na.action = na.pass, and no preprocessing (do not specify preProcess, leave it as its default value NULL). This will pass the NA values unmod |
8,618 | R caret and NAs | Have you tried recoding the NAs? Something >3 standard deviations outside your data (e.g. -12345) should encourage C5.0 to predict them separately, like it does with NAs. | R caret and NAs | Have you tried recoding the NAs? Something >3 standard deviations outside your data (e.g. -12345) should encourage C5.0 to predict them separately, like it does with NAs. | R caret and NAs
Have you tried recoding the NAs? Something >3 standard deviations outside your data (e.g. -12345) should encourage C5.0 to predict them separately, like it does with NAs. | R caret and NAs
Have you tried recoding the NAs? Something >3 standard deviations outside your data (e.g. -12345) should encourage C5.0 to predict them separately, like it does with NAs. |
8,619 | R caret and NAs | I think your solution would be to impute the values while using the predict() function.
See ?predict.train for more details.
You can use na.omit to allow caret to impute values. For example:
## S3 method for class 'train':
predict((object, newdata = NULL, type = "raw", na.action = na.omit, ...)
from http://www... | R caret and NAs | I think your solution would be to impute the values while using the predict() function.
See ?predict.train for more details.
You can use na.omit to allow caret to impute values. For example:
## S3 | R caret and NAs
I think your solution would be to impute the values while using the predict() function.
See ?predict.train for more details.
You can use na.omit to allow caret to impute values. For example:
## S3 method for class 'train':
predict((object, newdata = NULL, type = "raw", na.action = na.omit, ...)
... | R caret and NAs
I think your solution would be to impute the values while using the predict() function.
See ?predict.train for more details.
You can use na.omit to allow caret to impute values. For example:
## S3 |
8,620 | Is the exact value of a 'p-value' meaningless? | The type 1 / false rejection error rate $\alpha=.05$ isn't completely arbitrary, but yes, it is close. It's somewhat preferable to $\alpha=.051$ because it's less cognitively complex (people like round numbers and multiples of five). It's a decent compromise between skepticism and practicality, though maybe a little ou... | Is the exact value of a 'p-value' meaningless? | The type 1 / false rejection error rate $\alpha=.05$ isn't completely arbitrary, but yes, it is close. It's somewhat preferable to $\alpha=.051$ because it's less cognitively complex (people like roun | Is the exact value of a 'p-value' meaningless?
The type 1 / false rejection error rate $\alpha=.05$ isn't completely arbitrary, but yes, it is close. It's somewhat preferable to $\alpha=.051$ because it's less cognitively complex (people like round numbers and multiples of five). It's a decent compromise between skepti... | Is the exact value of a 'p-value' meaningless?
The type 1 / false rejection error rate $\alpha=.05$ isn't completely arbitrary, but yes, it is close. It's somewhat preferable to $\alpha=.051$ because it's less cognitively complex (people like roun |
8,621 | Is the exact value of a 'p-value' meaningless? | It seems to me that, if a value is meaningful, its exact value is meaningful.
The p value answers this question:
If, in the population from which this sample was randomly drawn, the
null hypothesis was true, what is the probability of getting a test
statistic at least as extreme as the one we got in the sample?
... | Is the exact value of a 'p-value' meaningless? | It seems to me that, if a value is meaningful, its exact value is meaningful.
The p value answers this question:
If, in the population from which this sample was randomly drawn, the
null hypothesi | Is the exact value of a 'p-value' meaningless?
It seems to me that, if a value is meaningful, its exact value is meaningful.
The p value answers this question:
If, in the population from which this sample was randomly drawn, the
null hypothesis was true, what is the probability of getting a test
statistic at leas... | Is the exact value of a 'p-value' meaningless?
It seems to me that, if a value is meaningful, its exact value is meaningful.
The p value answers this question:
If, in the population from which this sample was randomly drawn, the
null hypothesi |
8,622 | Intuitive reasoning behind biased maximum likelihood estimators | the ML estimator results in the value for the parameter which is most likely to occur in the dataset.
Given the assumptions, the ML estimator is the value of the parameter that has the best chance of producing the data set.
I cannot intuitively understand a biased ML estimator in the sense that "How can the most like... | Intuitive reasoning behind biased maximum likelihood estimators | the ML estimator results in the value for the parameter which is most likely to occur in the dataset.
Given the assumptions, the ML estimator is the value of the parameter that has the best chance of | Intuitive reasoning behind biased maximum likelihood estimators
the ML estimator results in the value for the parameter which is most likely to occur in the dataset.
Given the assumptions, the ML estimator is the value of the parameter that has the best chance of producing the data set.
I cannot intuitively understan... | Intuitive reasoning behind biased maximum likelihood estimators
the ML estimator results in the value for the parameter which is most likely to occur in the dataset.
Given the assumptions, the ML estimator is the value of the parameter that has the best chance of |
8,623 | Intuitive reasoning behind biased maximum likelihood estimators | $\beta^{MLE}$ is not the most probable value of $\beta$. The most probable value is $\beta $ itself. $\beta^{MLE}$ maximizes the probability of drawing the sample that we actually got.
MLE is only asymptotically unbiased, and often you can adjust the estimator to behave better in finite samples. For example, the MLE o... | Intuitive reasoning behind biased maximum likelihood estimators | $\beta^{MLE}$ is not the most probable value of $\beta$. The most probable value is $\beta $ itself. $\beta^{MLE}$ maximizes the probability of drawing the sample that we actually got.
MLE is only as | Intuitive reasoning behind biased maximum likelihood estimators
$\beta^{MLE}$ is not the most probable value of $\beta$. The most probable value is $\beta $ itself. $\beta^{MLE}$ maximizes the probability of drawing the sample that we actually got.
MLE is only asymptotically unbiased, and often you can adjust the esti... | Intuitive reasoning behind biased maximum likelihood estimators
$\beta^{MLE}$ is not the most probable value of $\beta$. The most probable value is $\beta $ itself. $\beta^{MLE}$ maximizes the probability of drawing the sample that we actually got.
MLE is only as |
8,624 | Intuitive reasoning behind biased maximum likelihood estimators | Here's my intuition.
Bias is a measure of accuracy, but there's also a notion of precision.
In an ideal world, we'd get the estimate, which is both precise and accurate, i.e. always hits the bull's eye. Unfortunately, in our imperfect world, we have to balance accuracy and precision. Sometimes we may feel that we co... | Intuitive reasoning behind biased maximum likelihood estimators | Here's my intuition.
Bias is a measure of accuracy, but there's also a notion of precision.
In an ideal world, we'd get the estimate, which is both precise and accurate, i.e. always hits the bull's | Intuitive reasoning behind biased maximum likelihood estimators
Here's my intuition.
Bias is a measure of accuracy, but there's also a notion of precision.
In an ideal world, we'd get the estimate, which is both precise and accurate, i.e. always hits the bull's eye. Unfortunately, in our imperfect world, we have to ... | Intuitive reasoning behind biased maximum likelihood estimators
Here's my intuition.
Bias is a measure of accuracy, but there's also a notion of precision.
In an ideal world, we'd get the estimate, which is both precise and accurate, i.e. always hits the bull's |
8,625 | Intuitive reasoning behind biased maximum likelihood estimators | The ordinary-language and technical meanings of biased are different. The answer by @Glen_b gives a good description of why maximum likelihood estimators can easily be biased in the technical sense.
It is possible for the maximum likelihood estimator to be biased in something like the ordinary-language sense, but it's ... | Intuitive reasoning behind biased maximum likelihood estimators | The ordinary-language and technical meanings of biased are different. The answer by @Glen_b gives a good description of why maximum likelihood estimators can easily be biased in the technical sense.
I | Intuitive reasoning behind biased maximum likelihood estimators
The ordinary-language and technical meanings of biased are different. The answer by @Glen_b gives a good description of why maximum likelihood estimators can easily be biased in the technical sense.
It is possible for the maximum likelihood estimator to be... | Intuitive reasoning behind biased maximum likelihood estimators
The ordinary-language and technical meanings of biased are different. The answer by @Glen_b gives a good description of why maximum likelihood estimators can easily be biased in the technical sense.
I |
8,626 | Are 50% confidence intervals more robustly estimated than 95% confidence intervals? | This answer analyzes the meaning of the quotation and offers the results of a simulation study to illustrate it and help understand what it might be trying to say. The study can easily be extended by anybody (with rudimentary R skills) to explore other confidence interval procedures and other models.
Two interesting i... | Are 50% confidence intervals more robustly estimated than 95% confidence intervals? | This answer analyzes the meaning of the quotation and offers the results of a simulation study to illustrate it and help understand what it might be trying to say. The study can easily be extended by | Are 50% confidence intervals more robustly estimated than 95% confidence intervals?
This answer analyzes the meaning of the quotation and offers the results of a simulation study to illustrate it and help understand what it might be trying to say. The study can easily be extended by anybody (with rudimentary R skills)... | Are 50% confidence intervals more robustly estimated than 95% confidence intervals?
This answer analyzes the meaning of the quotation and offers the results of a simulation study to illustrate it and help understand what it might be trying to say. The study can easily be extended by |
8,627 | Are 50% confidence intervals more robustly estimated than 95% confidence intervals? | This is an interesting idea, and I can see how it is intuitively compelling, but I think it is too vague to be true or false. Here are a couple of questions I would want the commenter to clear up:
A confidence interval for what (a mean, a variance, something else)?
How was the interval computed (using large sampl... | Are 50% confidence intervals more robustly estimated than 95% confidence intervals? | This is an interesting idea, and I can see how it is intuitively compelling, but I think it is too vague to be true or false. Here are a couple of questions I would want the commenter to clear up:
| Are 50% confidence intervals more robustly estimated than 95% confidence intervals?
This is an interesting idea, and I can see how it is intuitively compelling, but I think it is too vague to be true or false. Here are a couple of questions I would want the commenter to clear up:
A confidence interval for what (a m... | Are 50% confidence intervals more robustly estimated than 95% confidence intervals?
This is an interesting idea, and I can see how it is intuitively compelling, but I think it is too vague to be true or false. Here are a couple of questions I would want the commenter to clear up:
|
8,628 | How incorrect is a regression model when assumptions are not met? | What happens if the residuals are not homoscedastic? If the residuals show an increasing or decreasing pattern in Residuals vs. Fitted plot.
If the error term is not homoscedastic (we use the residuals as a proxy for the unobservable error term), the OLS estimator is still consistent and unbiased but is no longer the ... | How incorrect is a regression model when assumptions are not met? | What happens if the residuals are not homoscedastic? If the residuals show an increasing or decreasing pattern in Residuals vs. Fitted plot.
If the error term is not homoscedastic (we use the residua | How incorrect is a regression model when assumptions are not met?
What happens if the residuals are not homoscedastic? If the residuals show an increasing or decreasing pattern in Residuals vs. Fitted plot.
If the error term is not homoscedastic (we use the residuals as a proxy for the unobservable error term), the OL... | How incorrect is a regression model when assumptions are not met?
What happens if the residuals are not homoscedastic? If the residuals show an increasing or decreasing pattern in Residuals vs. Fitted plot.
If the error term is not homoscedastic (we use the residua |
8,629 | PCA, LDA, CCA, and PLS | Tijl De Bie wrote an interesting chapter "Eigenproblems in Pattern Recognition" which talks about exactly these from a primal/dual perspective. The three tables at the end summarise really nicely from an optimisation perspective: | PCA, LDA, CCA, and PLS | Tijl De Bie wrote an interesting chapter "Eigenproblems in Pattern Recognition" which talks about exactly these from a primal/dual perspective. The three tables at the end summarise really nicely from | PCA, LDA, CCA, and PLS
Tijl De Bie wrote an interesting chapter "Eigenproblems in Pattern Recognition" which talks about exactly these from a primal/dual perspective. The three tables at the end summarise really nicely from an optimisation perspective: | PCA, LDA, CCA, and PLS
Tijl De Bie wrote an interesting chapter "Eigenproblems in Pattern Recognition" which talks about exactly these from a primal/dual perspective. The three tables at the end summarise really nicely from |
8,630 | Why do we need to dummy code categorical variables? | Suppose your four categories are eye colors (code): brown (1), blue (2), green (3), hazel (4)βignoring heterochromia, violet, red, gray, etc. for the moment.
In no way (that I can currently imagine) would we mean that green $ = 3\times $ brown, or that hazel $=2\times$ blue as our codes imply, even though $3=3\times1$... | Why do we need to dummy code categorical variables? | Suppose your four categories are eye colors (code): brown (1), blue (2), green (3), hazel (4)βignoring heterochromia, violet, red, gray, etc. for the moment.
In no way (that I can currently imagine) | Why do we need to dummy code categorical variables?
Suppose your four categories are eye colors (code): brown (1), blue (2), green (3), hazel (4)βignoring heterochromia, violet, red, gray, etc. for the moment.
In no way (that I can currently imagine) would we mean that green $ = 3\times $ brown, or that hazel $=2\time... | Why do we need to dummy code categorical variables?
Suppose your four categories are eye colors (code): brown (1), blue (2), green (3), hazel (4)βignoring heterochromia, violet, red, gray, etc. for the moment.
In no way (that I can currently imagine) |
8,631 | Why do we need to dummy code categorical variables? | My take on this question is, that coding the four possible states with just two variables is less expressive with some machine learning algorithms than using 4 variables.
For example, imagine that you want to do linear regression and your true mapping maps the values 0,1 and 2 to 0 and the value 3 to 1. You can quickl... | Why do we need to dummy code categorical variables? | My take on this question is, that coding the four possible states with just two variables is less expressive with some machine learning algorithms than using 4 variables.
For example, imagine that yo | Why do we need to dummy code categorical variables?
My take on this question is, that coding the four possible states with just two variables is less expressive with some machine learning algorithms than using 4 variables.
For example, imagine that you want to do linear regression and your true mapping maps the values... | Why do we need to dummy code categorical variables?
My take on this question is, that coding the four possible states with just two variables is less expressive with some machine learning algorithms than using 4 variables.
For example, imagine that yo |
8,632 | Why do we need to dummy code categorical variables? | If you are to encode your example as:
Colour
Category
Binary1
Binary2
Hazel
0
0
0
Blue
1
0
1
Green
2
1
0
Brown
3
1
1
If your question is why do we use Encoding, lets consider the above example if you have 4 categories of colour of eyes according to what @Alexis suggested, we need to convert these cate... | Why do we need to dummy code categorical variables? | If you are to encode your example as:
Colour
Category
Binary1
Binary2
Hazel
0
0
0
Blue
1
0
1
Green
2
1
0
Brown
3
1
1
If your question is why do we use Encoding, lets consider the abo | Why do we need to dummy code categorical variables?
If you are to encode your example as:
Colour
Category
Binary1
Binary2
Hazel
0
0
0
Blue
1
0
1
Green
2
1
0
Brown
3
1
1
If your question is why do we use Encoding, lets consider the above example if you have 4 categories of colour of eyes according to w... | Why do we need to dummy code categorical variables?
If you are to encode your example as:
Colour
Category
Binary1
Binary2
Hazel
0
0
0
Blue
1
0
1
Green
2
1
0
Brown
3
1
1
If your question is why do we use Encoding, lets consider the abo |
8,633 | Why do we need to dummy code categorical variables? | Your alternative is also a dummy code. You choose the dummy code that best expresses the relationship to your dependent variable. Eg colour could be expressed as 1 of n, or you could turn into numeric rgb components, or you could categorise: girly/muddy/...1 of n basically means each instance is learnt separately which... | Why do we need to dummy code categorical variables? | Your alternative is also a dummy code. You choose the dummy code that best expresses the relationship to your dependent variable. Eg colour could be expressed as 1 of n, or you could turn into numeric | Why do we need to dummy code categorical variables?
Your alternative is also a dummy code. You choose the dummy code that best expresses the relationship to your dependent variable. Eg colour could be expressed as 1 of n, or you could turn into numeric rgb components, or you could categorise: girly/muddy/...1 of n basi... | Why do we need to dummy code categorical variables?
Your alternative is also a dummy code. You choose the dummy code that best expresses the relationship to your dependent variable. Eg colour could be expressed as 1 of n, or you could turn into numeric |
8,634 | Why don't Bayesian methods require multiple testing corrections? | One odd way to answer the question is to note that the Bayesian method provides no way to do this because Bayesian methods are consistent with accepted rules of evidence and frequentist methods are often at odds with them. Examples:
With frequentist statistics, comparing treatment A to B must penalize for comparing t... | Why don't Bayesian methods require multiple testing corrections? | One odd way to answer the question is to note that the Bayesian method provides no way to do this because Bayesian methods are consistent with accepted rules of evidence and frequentist methods are of | Why don't Bayesian methods require multiple testing corrections?
One odd way to answer the question is to note that the Bayesian method provides no way to do this because Bayesian methods are consistent with accepted rules of evidence and frequentist methods are often at odds with them. Examples:
With frequentist sta... | Why don't Bayesian methods require multiple testing corrections?
One odd way to answer the question is to note that the Bayesian method provides no way to do this because Bayesian methods are consistent with accepted rules of evidence and frequentist methods are of |
8,635 | Why don't Bayesian methods require multiple testing corrections? | This type of hierarchical model does shrink the estimates and reduces the number of false claims to a reasonable extent for a small to moderate number of hypotheses. Does it guarantee some specific type I error rate? No.
This particular suggestion by Gelman (who acknowledges the issue with looking at too many differen... | Why don't Bayesian methods require multiple testing corrections? | This type of hierarchical model does shrink the estimates and reduces the number of false claims to a reasonable extent for a small to moderate number of hypotheses. Does it guarantee some specific ty | Why don't Bayesian methods require multiple testing corrections?
This type of hierarchical model does shrink the estimates and reduces the number of false claims to a reasonable extent for a small to moderate number of hypotheses. Does it guarantee some specific type I error rate? No.
This particular suggestion by Gel... | Why don't Bayesian methods require multiple testing corrections?
This type of hierarchical model does shrink the estimates and reduces the number of false claims to a reasonable extent for a small to moderate number of hypotheses. Does it guarantee some specific ty |
8,636 | Why don't Bayesian methods require multiple testing corrections? | Very interesting question, here's my take on it.
It's all about encoding information, then turn the Bayesian crank. It seems too good to be true - but both of these are harder than they seem.
I start with asking the question
What information is being used when we worry about multiple comparisons?
I can think of some -... | Why don't Bayesian methods require multiple testing corrections? | Very interesting question, here's my take on it.
It's all about encoding information, then turn the Bayesian crank. It seems too good to be true - but both of these are harder than they seem.
I start | Why don't Bayesian methods require multiple testing corrections?
Very interesting question, here's my take on it.
It's all about encoding information, then turn the Bayesian crank. It seems too good to be true - but both of these are harder than they seem.
I start with asking the question
What information is being use... | Why don't Bayesian methods require multiple testing corrections?
Very interesting question, here's my take on it.
It's all about encoding information, then turn the Bayesian crank. It seems too good to be true - but both of these are harder than they seem.
I start |
8,637 | Why don't Bayesian methods require multiple testing corrections? | First, as I understand the model you presented I think it is a bit different to Gelman proposal, that more looks like:
A ~ Distribution(locationA)
B ~ Distribution(locationB)
C ~ Distribution(locationC)
locationA ~ Normal(commonLocation)
locationB ~ Normal(commonLocation)
locationC ~ Normal(commonLocation)
commonLoca... | Why don't Bayesian methods require multiple testing corrections? | First, as I understand the model you presented I think it is a bit different to Gelman proposal, that more looks like:
A ~ Distribution(locationA)
B ~ Distribution(locationB)
C ~ Distribution(location | Why don't Bayesian methods require multiple testing corrections?
First, as I understand the model you presented I think it is a bit different to Gelman proposal, that more looks like:
A ~ Distribution(locationA)
B ~ Distribution(locationB)
C ~ Distribution(locationC)
locationA ~ Normal(commonLocation)
locationB ~ Norm... | Why don't Bayesian methods require multiple testing corrections?
First, as I understand the model you presented I think it is a bit different to Gelman proposal, that more looks like:
A ~ Distribution(locationA)
B ~ Distribution(locationB)
C ~ Distribution(location |
8,638 | t-test for partially paired and partially unpaired data | Guo and Yuan suggest an alternative method called the optimal pooled t-test stemming from Samawi and Vogel's pooled t-test.
Link to reference:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.865.734&rep=rep1&type=pdf
Great read with multiple options for this situation.
New to commenting so please let me know i... | t-test for partially paired and partially unpaired data | Guo and Yuan suggest an alternative method called the optimal pooled t-test stemming from Samawi and Vogel's pooled t-test.
Link to reference:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1. | t-test for partially paired and partially unpaired data
Guo and Yuan suggest an alternative method called the optimal pooled t-test stemming from Samawi and Vogel's pooled t-test.
Link to reference:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.865.734&rep=rep1&type=pdf
Great read with multiple options for t... | t-test for partially paired and partially unpaired data
Guo and Yuan suggest an alternative method called the optimal pooled t-test stemming from Samawi and Vogel's pooled t-test.
Link to reference:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1. |
8,639 | t-test for partially paired and partially unpaired data | Well, if you knew the variances in the unpaired and in the paired (which would generally be a good deal smaller), the optimal weights for the two estimates of difference in groups means would be to have weights inversely proportional to the variance of the individual estimates of the difference in means.
[Edit: turns o... | t-test for partially paired and partially unpaired data | Well, if you knew the variances in the unpaired and in the paired (which would generally be a good deal smaller), the optimal weights for the two estimates of difference in groups means would be to ha | t-test for partially paired and partially unpaired data
Well, if you knew the variances in the unpaired and in the paired (which would generally be a good deal smaller), the optimal weights for the two estimates of difference in groups means would be to have weights inversely proportional to the variance of the individ... | t-test for partially paired and partially unpaired data
Well, if you knew the variances in the unpaired and in the paired (which would generally be a good deal smaller), the optimal weights for the two estimates of difference in groups means would be to ha |
8,640 | t-test for partially paired and partially unpaired data | Here are some thoughts. I basically just arrive to Greg Snow conclusion that This problem has distinct similarities to the Behrens-Fisher problem. To avoid handwaving I first introduce some notations and formalize the hypotheses.
we have $n$ paired observations $x_i^{pA}$ and $x_i^{pB}$ ($i = 1, \dots, n$);
we have $n... | t-test for partially paired and partially unpaired data | Here are some thoughts. I basically just arrive to Greg Snow conclusion that This problem has distinct similarities to the Behrens-Fisher problem. To avoid handwaving I first introduce some notations | t-test for partially paired and partially unpaired data
Here are some thoughts. I basically just arrive to Greg Snow conclusion that This problem has distinct similarities to the Behrens-Fisher problem. To avoid handwaving I first introduce some notations and formalize the hypotheses.
we have $n$ paired observations $... | t-test for partially paired and partially unpaired data
Here are some thoughts. I basically just arrive to Greg Snow conclusion that This problem has distinct similarities to the Behrens-Fisher problem. To avoid handwaving I first introduce some notations |
8,641 | t-test for partially paired and partially unpaired data | My first thought was a mixed effects model, but that has already been discussed so I won't say any more on that.
My other thought is that if it were theoretically possible that you could have measured paired data on all subjects but due to cost, errors, or another reason you don't have all the pairs, then you could tre... | t-test for partially paired and partially unpaired data | My first thought was a mixed effects model, but that has already been discussed so I won't say any more on that.
My other thought is that if it were theoretically possible that you could have measured | t-test for partially paired and partially unpaired data
My first thought was a mixed effects model, but that has already been discussed so I won't say any more on that.
My other thought is that if it were theoretically possible that you could have measured paired data on all subjects but due to cost, errors, or another... | t-test for partially paired and partially unpaired data
My first thought was a mixed effects model, but that has already been discussed so I won't say any more on that.
My other thought is that if it were theoretically possible that you could have measured |
8,642 | t-test for partially paired and partially unpaired data | maybe mixed modelling with patient as random effect could be a way. With mixed modelling the correlation structure in the paired case and the partial missings in the unpaired case could be accounted for. | t-test for partially paired and partially unpaired data | maybe mixed modelling with patient as random effect could be a way. With mixed modelling the correlation structure in the paired case and the partial missings in the unpaired case could be accounted f | t-test for partially paired and partially unpaired data
maybe mixed modelling with patient as random effect could be a way. With mixed modelling the correlation structure in the paired case and the partial missings in the unpaired case could be accounted for. | t-test for partially paired and partially unpaired data
maybe mixed modelling with patient as random effect could be a way. With mixed modelling the correlation structure in the paired case and the partial missings in the unpaired case could be accounted f |
8,643 | t-test for partially paired and partially unpaired data | One of the methods proposed in Hani M. Samawi & Robert Vogel (Journal of Applied Statistics , 2013) consists of a weighted combination of T-scores from independent and dependent samples in such a way that the new T score is equal to
$T_o = \sqrt\gamma ( \frac {\mu_Y - \mu_X} {S_x^2/n_X + S_y^2/n_Y}) + \sqrt {(1-\gamma)... | t-test for partially paired and partially unpaired data | One of the methods proposed in Hani M. Samawi & Robert Vogel (Journal of Applied Statistics , 2013) consists of a weighted combination of T-scores from independent and dependent samples in such a way | t-test for partially paired and partially unpaired data
One of the methods proposed in Hani M. Samawi & Robert Vogel (Journal of Applied Statistics , 2013) consists of a weighted combination of T-scores from independent and dependent samples in such a way that the new T score is equal to
$T_o = \sqrt\gamma ( \frac {\mu... | t-test for partially paired and partially unpaired data
One of the methods proposed in Hani M. Samawi & Robert Vogel (Journal of Applied Statistics , 2013) consists of a weighted combination of T-scores from independent and dependent samples in such a way |
8,644 | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering | Great question. Anything can be good or bad, useful or not, based on what your goals are (and perhaps on the nature of your situation). For the most part, these methods are designed to satisfy different goals.
Statistical tests, like the $t$-test allow you to test scientific hypotheses. They are often used for ot... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica | Great question. Anything can be good or bad, useful or not, based on what your goals are (and perhaps on the nature of your situation). For the most part, these methods are designed to satisfy diffe | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering
Great question. Anything can be good or bad, useful or not, based on what your goals are (and perhaps on the nature of your situation). For the most part, these methods are designed to satisfy diffe... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica
Great question. Anything can be good or bad, useful or not, based on what your goals are (and perhaps on the nature of your situation). For the most part, these methods are designed to satisfy diffe |
8,645 | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering | Not going to address clustering because it's been addressed in other answers, but:
In general, the problem of testing whether two samples are meaningfully different is known as two-sample testing.
By doing a $t$-test, you severely limit the kinds of differences that you're looking for (differences in means between norm... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica | Not going to address clustering because it's been addressed in other answers, but:
In general, the problem of testing whether two samples are meaningfully different is known as two-sample testing.
By | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering
Not going to address clustering because it's been addressed in other answers, but:
In general, the problem of testing whether two samples are meaningfully different is known as two-sample testing.
By ... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica
Not going to address clustering because it's been addressed in other answers, but:
In general, the problem of testing whether two samples are meaningfully different is known as two-sample testing.
By |
8,646 | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering | Only approach (a) serves the purpose of testing hypothesis.
In case of using supervised machine learning algorithms (b), they cannot neither prove or disprove hypothesis about distingness of groups. If machine learning algorithm does not classify the groups correctly it may happen because you used "wrong" algorithm for... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica | Only approach (a) serves the purpose of testing hypothesis.
In case of using supervised machine learning algorithms (b), they cannot neither prove or disprove hypothesis about distingness of groups. I | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering
Only approach (a) serves the purpose of testing hypothesis.
In case of using supervised machine learning algorithms (b), they cannot neither prove or disprove hypothesis about distingness of groups. I... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica
Only approach (a) serves the purpose of testing hypothesis.
In case of using supervised machine learning algorithms (b), they cannot neither prove or disprove hypothesis about distingness of groups. I |
8,647 | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering | a) only answers you the question whether the distribution is different, but not how to distinguish them. b) will also find the best value to differentiate between the two distributions. c) will work if the two distributions have some specific properties. For example it will work with normal distribution but not with so... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica | a) only answers you the question whether the distribution is different, but not how to distinguish them. b) will also find the best value to differentiate between the two distributions. c) will work i | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering
a) only answers you the question whether the distribution is different, but not how to distinguish them. b) will also find the best value to differentiate between the two distributions. c) will work i... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica
a) only answers you the question whether the distribution is different, but not how to distinguish them. b) will also find the best value to differentiate between the two distributions. c) will work i |
8,648 | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering | Statistical testing is for making inference from data, it tells you how things are related. The result is something that has a real-world meaning. E.g. how smoking is associated with lung cancer, both in terms of direction and magnitude. It still does not tell you why things happened. To answer why things happened, we ... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica | Statistical testing is for making inference from data, it tells you how things are related. The result is something that has a real-world meaning. E.g. how smoking is associated with lung cancer, both | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering
Statistical testing is for making inference from data, it tells you how things are related. The result is something that has a real-world meaning. E.g. how smoking is associated with lung cancer, both... | Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classifica
Statistical testing is for making inference from data, it tells you how things are related. The result is something that has a real-world meaning. E.g. how smoking is associated with lung cancer, both |
8,649 | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time? | This would obviously be an absolute nightmare to do in practice, but suppose it could be done: we appoint a Statistical Sultan and everyone running a hypothesis test reports their raw $p$-values to this despot. He performs some kind of global (literally) multiple comparisons correction and replies with the corrected ve... | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time? | This would obviously be an absolute nightmare to do in practice, but suppose it could be done: we appoint a Statistical Sultan and everyone running a hypothesis test reports their raw $p$-values to th | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time?
This would obviously be an absolute nightmare to do in practice, but suppose it could be done: we appoint a Statistical Sultan and everyone running a hypothesis test reports their raw $p$-values to this despot. He performs som... | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time?
This would obviously be an absolute nightmare to do in practice, but suppose it could be done: we appoint a Statistical Sultan and everyone running a hypothesis test reports their raw $p$-values to th |
8,650 | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time? | I think that you deliberately paint a pessimistic view of science produced by statistics. Indeed, in my opinion, statistics is not just a set of tools providing p values. There is also a state of rigour, care and alertness about some possible effects involved in the procedure of scientific induction... and while to my ... | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time? | I think that you deliberately paint a pessimistic view of science produced by statistics. Indeed, in my opinion, statistics is not just a set of tools providing p values. There is also a state of rigo | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time?
I think that you deliberately paint a pessimistic view of science produced by statistics. Indeed, in my opinion, statistics is not just a set of tools providing p values. There is also a state of rigour, care and alertness abo... | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time?
I think that you deliberately paint a pessimistic view of science produced by statistics. Indeed, in my opinion, statistics is not just a set of tools providing p values. There is also a state of rigo |
8,651 | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time? | Is it possible to control the false discovery rate without applying some such correction?
Yes. This is what a threshold on p values does: it sets the rate of false discoveries to that threshold. In the long run, on all tests run on true null hypotheses, only $100\,a$ percent will have a p value below $a$.
Remember tha... | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time? | Is it possible to control the false discovery rate without applying some such correction?
Yes. This is what a threshold on p values does: it sets the rate of false discoveries to that threshold. In t | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time?
Is it possible to control the false discovery rate without applying some such correction?
Yes. This is what a threshold on p values does: it sets the rate of false discoveries to that threshold. In the long run, on all tests ... | Why aren't multiple hypothesis corrections applied to all experiments since the dawn of time?
Is it possible to control the false discovery rate without applying some such correction?
Yes. This is what a threshold on p values does: it sets the rate of false discoveries to that threshold. In t |
8,652 | What are variable importance rankings useful for? | I have argued that variable importance is a slippery concept, as this question posits. The tautological first type of response that you get to your question and the unrealistic hopes of those who would interpret variable-importance results in terms of causality, as noted by @DexGroves, need little elaboration.
In fairn... | What are variable importance rankings useful for? | I have argued that variable importance is a slippery concept, as this question posits. The tautological first type of response that you get to your question and the unrealistic hopes of those who woul | What are variable importance rankings useful for?
I have argued that variable importance is a slippery concept, as this question posits. The tautological first type of response that you get to your question and the unrealistic hopes of those who would interpret variable-importance results in terms of causality, as note... | What are variable importance rankings useful for?
I have argued that variable importance is a slippery concept, as this question posits. The tautological first type of response that you get to your question and the unrealistic hopes of those who woul |
8,653 | What are variable importance rankings useful for? | This is completely anecdotal, but I've found variable importance useful in identifying mistakes or weaknesses in GBMs.
Variable importance gives you a kind of huge cross-sectional overview of the model that would be hard to get otherwise. Variables higher in the list are seeing more activity (whether or not they are m... | What are variable importance rankings useful for? | This is completely anecdotal, but I've found variable importance useful in identifying mistakes or weaknesses in GBMs.
Variable importance gives you a kind of huge cross-sectional overview of the mod | What are variable importance rankings useful for?
This is completely anecdotal, but I've found variable importance useful in identifying mistakes or weaknesses in GBMs.
Variable importance gives you a kind of huge cross-sectional overview of the model that would be hard to get otherwise. Variables higher in the list a... | What are variable importance rankings useful for?
This is completely anecdotal, but I've found variable importance useful in identifying mistakes or weaknesses in GBMs.
Variable importance gives you a kind of huge cross-sectional overview of the mod |
8,654 | What are variable importance rankings useful for? | Variable importance rankings have a definite role in the applied business world whenever there is a need to prioritize the potentially large number of inputs to a process, any process. This information provides direction in terms of a focused strategy for attacking a problem, working down from most to least important, ... | What are variable importance rankings useful for? | Variable importance rankings have a definite role in the applied business world whenever there is a need to prioritize the potentially large number of inputs to a process, any process. This informatio | What are variable importance rankings useful for?
Variable importance rankings have a definite role in the applied business world whenever there is a need to prioritize the potentially large number of inputs to a process, any process. This information provides direction in terms of a focused strategy for attacking a pr... | What are variable importance rankings useful for?
Variable importance rankings have a definite role in the applied business world whenever there is a need to prioritize the potentially large number of inputs to a process, any process. This informatio |
8,655 | What are variable importance rankings useful for? | I'm completely agree with you in the theoretical point of view. But in the practical point of view, variable importance is very useful.
Let's take an example in which an insurance company wants to reduce the number of questions in a questionnaire quantifying the risk of their clients. The more complicated the questionn... | What are variable importance rankings useful for? | I'm completely agree with you in the theoretical point of view. But in the practical point of view, variable importance is very useful.
Let's take an example in which an insurance company wants to red | What are variable importance rankings useful for?
I'm completely agree with you in the theoretical point of view. But in the practical point of view, variable importance is very useful.
Let's take an example in which an insurance company wants to reduce the number of questions in a questionnaire quantifying the risk of... | What are variable importance rankings useful for?
I'm completely agree with you in the theoretical point of view. But in the practical point of view, variable importance is very useful.
Let's take an example in which an insurance company wants to red |
8,656 | How to compute SVD of a huge sparse matrix? | If it fits into memory, construct a sparse matrix in R using the Matrix package, and try irlba for the SVD. You can specify how many singular vectors you want in the result, which is another way to limit the computation.
That's a pretty big matrix, but I've had very good results with this method in the past. irlba is... | How to compute SVD of a huge sparse matrix? | If it fits into memory, construct a sparse matrix in R using the Matrix package, and try irlba for the SVD. You can specify how many singular vectors you want in the result, which is another way to l | How to compute SVD of a huge sparse matrix?
If it fits into memory, construct a sparse matrix in R using the Matrix package, and try irlba for the SVD. You can specify how many singular vectors you want in the result, which is another way to limit the computation.
That's a pretty big matrix, but I've had very good res... | How to compute SVD of a huge sparse matrix?
If it fits into memory, construct a sparse matrix in R using the Matrix package, and try irlba for the SVD. You can specify how many singular vectors you want in the result, which is another way to l |
8,657 | How to compute SVD of a huge sparse matrix? | If you're willing to have a low-rank approximation (as you would with Lanczos-type algorithms and a limited number of singular vectors), an alternative is stochastic SVD. You get similar accuracy and computational effort to things like irlba, but a much, much simpler implementation -- which is relevant if none of the ... | How to compute SVD of a huge sparse matrix? | If you're willing to have a low-rank approximation (as you would with Lanczos-type algorithms and a limited number of singular vectors), an alternative is stochastic SVD. You get similar accuracy and | How to compute SVD of a huge sparse matrix?
If you're willing to have a low-rank approximation (as you would with Lanczos-type algorithms and a limited number of singular vectors), an alternative is stochastic SVD. You get similar accuracy and computational effort to things like irlba, but a much, much simpler impleme... | How to compute SVD of a huge sparse matrix?
If you're willing to have a low-rank approximation (as you would with Lanczos-type algorithms and a limited number of singular vectors), an alternative is stochastic SVD. You get similar accuracy and |
8,658 | How do you deal with "nested" variables in a regression model? | Meaningless values of nested variables must not affect your model:
The crucial desideratum with this type of data analysis is that the nested variable must not impact the model if the original explanatory variable does not admit it as a meaningful variable. In other words, the model must be of a form that ignores mean... | How do you deal with "nested" variables in a regression model? | Meaningless values of nested variables must not affect your model:
The crucial desideratum with this type of data analysis is that the nested variable must not impact the model if the original explana | How do you deal with "nested" variables in a regression model?
Meaningless values of nested variables must not affect your model:
The crucial desideratum with this type of data analysis is that the nested variable must not impact the model if the original explanatory variable does not admit it as a meaningful variable.... | How do you deal with "nested" variables in a regression model?
Meaningless values of nested variables must not affect your model:
The crucial desideratum with this type of data analysis is that the nested variable must not impact the model if the original explana |
8,659 | libsvm data format [closed] | This link should help:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q3:_Data_preparation
It's mentioned that the data is stored in a sparse array/matrix form. Essentially, it means only the non-zero data are stored, and any missing data is taken as holding value zero. For your questions:
a) Index merely serves as... | libsvm data format [closed] | This link should help:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q3:_Data_preparation
It's mentioned that the data is stored in a sparse array/matrix form. Essentially, it means only the non- | libsvm data format [closed]
This link should help:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q3:_Data_preparation
It's mentioned that the data is stored in a sparse array/matrix form. Essentially, it means only the non-zero data are stored, and any missing data is taken as holding value zero. For your question... | libsvm data format [closed]
This link should help:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#/Q3:_Data_preparation
It's mentioned that the data is stored in a sparse array/matrix form. Essentially, it means only the non- |
8,660 | libsvm data format [closed] | Just small and fast guide:
LibSVM format means that your document needs to be pre-processed already. You need to know how many classification classes will be used (most likely 2) and feature space.
Classification class is something like true/false; 0,1,... Here you need to transform it into integers (e.g. 0,1).
Featur... | libsvm data format [closed] | Just small and fast guide:
LibSVM format means that your document needs to be pre-processed already. You need to know how many classification classes will be used (most likely 2) and feature space.
C | libsvm data format [closed]
Just small and fast guide:
LibSVM format means that your document needs to be pre-processed already. You need to know how many classification classes will be used (most likely 2) and feature space.
Classification class is something like true/false; 0,1,... Here you need to transform it into... | libsvm data format [closed]
Just small and fast guide:
LibSVM format means that your document needs to be pre-processed already. You need to know how many classification classes will be used (most likely 2) and feature space.
C |
8,661 | Why does the Kolmogorov-Smirnov test work? | Basically, the test is consistent as a direct result of the Glivenko Cantelli theorem, one of the most important results of empirical processes and maybe statistics.
GC tells us that the Kolmogorov Smirnov test statistic goes to 0 as $n \rightarrow \infty$ under the null hypothesis. It may seem intuitive until you grap... | Why does the Kolmogorov-Smirnov test work? | Basically, the test is consistent as a direct result of the Glivenko Cantelli theorem, one of the most important results of empirical processes and maybe statistics.
GC tells us that the Kolmogorov Sm | Why does the Kolmogorov-Smirnov test work?
Basically, the test is consistent as a direct result of the Glivenko Cantelli theorem, one of the most important results of empirical processes and maybe statistics.
GC tells us that the Kolmogorov Smirnov test statistic goes to 0 as $n \rightarrow \infty$ under the null hypot... | Why does the Kolmogorov-Smirnov test work?
Basically, the test is consistent as a direct result of the Glivenko Cantelli theorem, one of the most important results of empirical processes and maybe statistics.
GC tells us that the Kolmogorov Sm |
8,662 | Why does the Kolmogorov-Smirnov test work? | We have two independent, univariate samples:
\begin{align}
X_1,\,X_2,\,...,\,X_N&\overset{iid}{\sim}F\\
Y_1,\,Y_2,\,...,\,Y_M&\overset{iid}{\sim}G,
\end{align}
where $G$ and $F$ are continuous cumulative distribution functions. The Kolmogorov-Smirnov test is testing
\begin{align}
H_0&:F(x) = G(x)\quad\text{for all } x\... | Why does the Kolmogorov-Smirnov test work? | We have two independent, univariate samples:
\begin{align}
X_1,\,X_2,\,...,\,X_N&\overset{iid}{\sim}F\\
Y_1,\,Y_2,\,...,\,Y_M&\overset{iid}{\sim}G,
\end{align}
where $G$ and $F$ are continuous cumulat | Why does the Kolmogorov-Smirnov test work?
We have two independent, univariate samples:
\begin{align}
X_1,\,X_2,\,...,\,X_N&\overset{iid}{\sim}F\\
Y_1,\,Y_2,\,...,\,Y_M&\overset{iid}{\sim}G,
\end{align}
where $G$ and $F$ are continuous cumulative distribution functions. The Kolmogorov-Smirnov test is testing
\begin{ali... | Why does the Kolmogorov-Smirnov test work?
We have two independent, univariate samples:
\begin{align}
X_1,\,X_2,\,...,\,X_N&\overset{iid}{\sim}F\\
Y_1,\,Y_2,\,...,\,Y_M&\overset{iid}{\sim}G,
\end{align}
where $G$ and $F$ are continuous cumulat |
8,663 | Why does the Kolmogorov-Smirnov test work? | An intuitive take:
The Kolmogorov-Smirnov test relies pretty fundamentally on the ordering of observations by distribution. The logic is that if the two underlying distributions are the same, thenβdependent on sample sizesβthe ordering should be pretty well shuffled between the two.
If the sample ordering is "unshuffl... | Why does the Kolmogorov-Smirnov test work? | An intuitive take:
The Kolmogorov-Smirnov test relies pretty fundamentally on the ordering of observations by distribution. The logic is that if the two underlying distributions are the same, thenβde | Why does the Kolmogorov-Smirnov test work?
An intuitive take:
The Kolmogorov-Smirnov test relies pretty fundamentally on the ordering of observations by distribution. The logic is that if the two underlying distributions are the same, thenβdependent on sample sizesβthe ordering should be pretty well shuffled between t... | Why does the Kolmogorov-Smirnov test work?
An intuitive take:
The Kolmogorov-Smirnov test relies pretty fundamentally on the ordering of observations by distribution. The logic is that if the two underlying distributions are the same, thenβde |
8,664 | When if ever is a median statistic a sufficient statistic? | In the case when the support of the distribution does not depend on the unknown parameter $\theta, $ we can invoke the (FrΓ©chet-Darmois-)Pitman-Koopman theorem, namely that the density of the observations is necessarily of the exponential family form,
$$
\exp\{ \theta T(x) - \psi(\theta) \}h(x)
$$
to conclude that, sin... | When if ever is a median statistic a sufficient statistic? | In the case when the support of the distribution does not depend on the unknown parameter $\theta, $ we can invoke the (FrΓ©chet-Darmois-)Pitman-Koopman theorem, namely that the density of the observat | When if ever is a median statistic a sufficient statistic?
In the case when the support of the distribution does not depend on the unknown parameter $\theta, $ we can invoke the (FrΓ©chet-Darmois-)Pitman-Koopman theorem, namely that the density of the observations is necessarily of the exponential family form,
$$
\exp\{... | When if ever is a median statistic a sufficient statistic?
In the case when the support of the distribution does not depend on the unknown parameter $\theta, $ we can invoke the (FrΓ©chet-Darmois-)Pitman-Koopman theorem, namely that the density of the observat |
8,665 | When if ever is a median statistic a sufficient statistic? | Xi'an's answer raises the question of what is happening with the Laplace (double exponential distribution), where the MLE is the median. As Xi'an says, the median is not sufficient in this model; it can't be, because among families with constant support only exponential families have finite-dimensional sufficient stati... | When if ever is a median statistic a sufficient statistic? | Xi'an's answer raises the question of what is happening with the Laplace (double exponential distribution), where the MLE is the median. As Xi'an says, the median is not sufficient in this model; it c | When if ever is a median statistic a sufficient statistic?
Xi'an's answer raises the question of what is happening with the Laplace (double exponential distribution), where the MLE is the median. As Xi'an says, the median is not sufficient in this model; it can't be, because among families with constant support only ex... | When if ever is a median statistic a sufficient statistic?
Xi'an's answer raises the question of what is happening with the Laplace (double exponential distribution), where the MLE is the median. As Xi'an says, the median is not sufficient in this model; it c |
8,666 | Bias correction in weighted variance | I went through the math and ended up with variant C:
$$Var(X) = \frac{(\sum_i \omega_i)^2}{(\sum_i \omega_i)^2 - \sum_i \omega_i^2}\overline V$$
where $\overline V$ is the non corrected variance estimation. The formula agrees with the unweighted case when all $\omega_i$ are identical. I detail the proof below:
Setting ... | Bias correction in weighted variance | I went through the math and ended up with variant C:
$$Var(X) = \frac{(\sum_i \omega_i)^2}{(\sum_i \omega_i)^2 - \sum_i \omega_i^2}\overline V$$
where $\overline V$ is the non corrected variance estim | Bias correction in weighted variance
I went through the math and ended up with variant C:
$$Var(X) = \frac{(\sum_i \omega_i)^2}{(\sum_i \omega_i)^2 - \sum_i \omega_i^2}\overline V$$
where $\overline V$ is the non corrected variance estimation. The formula agrees with the unweighted case when all $\omega_i$ are identica... | Bias correction in weighted variance
I went through the math and ended up with variant C:
$$Var(X) = \frac{(\sum_i \omega_i)^2}{(\sum_i \omega_i)^2 - \sum_i \omega_i^2}\overline V$$
where $\overline V$ is the non corrected variance estim |
8,667 | Bias correction in weighted variance | Both A and C are correct, but which one you will use depends on what kind of weights you use:
A needs you to use "repeat"-type weights (integers counting the number of occurrences for each observation), and is unbiased.
C needs you to use "reliability"-type weights (either normalized weights or either variances for ea... | Bias correction in weighted variance | Both A and C are correct, but which one you will use depends on what kind of weights you use:
A needs you to use "repeat"-type weights (integers counting the number of occurrences for each observatio | Bias correction in weighted variance
Both A and C are correct, but which one you will use depends on what kind of weights you use:
A needs you to use "repeat"-type weights (integers counting the number of occurrences for each observation), and is unbiased.
C needs you to use "reliability"-type weights (either normaliz... | Bias correction in weighted variance
Both A and C are correct, but which one you will use depends on what kind of weights you use:
A needs you to use "repeat"-type weights (integers counting the number of occurrences for each observatio |
8,668 | How to rigorously define the likelihood? | Your third item is the one I have seen the most often used as rigorous definition.
The others are interesting too (+1). In particular the first is appealing, with the difficulty that the sample size not being (yet) defined, it is harder to define the "from" set.
To me, the fundamental intuition of the likelihood is th... | How to rigorously define the likelihood? | Your third item is the one I have seen the most often used as rigorous definition.
The others are interesting too (+1). In particular the first is appealing, with the difficulty that the sample size | How to rigorously define the likelihood?
Your third item is the one I have seen the most often used as rigorous definition.
The others are interesting too (+1). In particular the first is appealing, with the difficulty that the sample size not being (yet) defined, it is harder to define the "from" set.
To me, the fund... | How to rigorously define the likelihood?
Your third item is the one I have seen the most often used as rigorous definition.
The others are interesting too (+1). In particular the first is appealing, with the difficulty that the sample size |
8,669 | How to rigorously define the likelihood? | I think I would call it something different. Likelihood is the probability density for the observed x given the value of the parameter $ΞΈ$ expressed as a function of $ΞΈ$ for the given $x$. I don't share the view about the proportionality constant. I think that only comes into play because maximizing any monotonic fu... | How to rigorously define the likelihood? | I think I would call it something different. Likelihood is the probability density for the observed x given the value of the parameter $ΞΈ$ expressed as a function of $ΞΈ$ for the given $x$. I don't s | How to rigorously define the likelihood?
I think I would call it something different. Likelihood is the probability density for the observed x given the value of the parameter $ΞΈ$ expressed as a function of $ΞΈ$ for the given $x$. I don't share the view about the proportionality constant. I think that only comes into... | How to rigorously define the likelihood?
I think I would call it something different. Likelihood is the probability density for the observed x given the value of the parameter $ΞΈ$ expressed as a function of $ΞΈ$ for the given $x$. I don't s |
8,670 | How to rigorously define the likelihood? | Here's an attempt at a rigorous mathematical definition:
Let $X: \Omega \to \mathbb R^n$ be a random vector which admits a density $f(x | \theta_0)$ with respect to some measure $\nu$ on $\mathbb R^n$, where for $\theta \in \Theta$, $\{f(x|\theta): \theta \in \Theta\}$ is a family of densities on $\mathbb R^n$ with res... | How to rigorously define the likelihood? | Here's an attempt at a rigorous mathematical definition:
Let $X: \Omega \to \mathbb R^n$ be a random vector which admits a density $f(x | \theta_0)$ with respect to some measure $\nu$ on $\mathbb R^n$ | How to rigorously define the likelihood?
Here's an attempt at a rigorous mathematical definition:
Let $X: \Omega \to \mathbb R^n$ be a random vector which admits a density $f(x | \theta_0)$ with respect to some measure $\nu$ on $\mathbb R^n$, where for $\theta \in \Theta$, $\{f(x|\theta): \theta \in \Theta\}$ is a fami... | How to rigorously define the likelihood?
Here's an attempt at a rigorous mathematical definition:
Let $X: \Omega \to \mathbb R^n$ be a random vector which admits a density $f(x | \theta_0)$ with respect to some measure $\nu$ on $\mathbb R^n$ |
8,671 | How to test and avoid multicollinearity in mixed linear model? | For VIF calculation usdm can also be package ( I need to install "usdm")
library(usdm)
df = # Data Frame
vif(df)
If VIF > 4.0 then I generally assume multicollinearity remove all those Predictor Variables before fitting them into my model | How to test and avoid multicollinearity in mixed linear model? | For VIF calculation usdm can also be package ( I need to install "usdm")
library(usdm)
df = # Data Frame
vif(df)
If VIF > 4.0 then I generally assume multicollinearity remove all those Predictor Var | How to test and avoid multicollinearity in mixed linear model?
For VIF calculation usdm can also be package ( I need to install "usdm")
library(usdm)
df = # Data Frame
vif(df)
If VIF > 4.0 then I generally assume multicollinearity remove all those Predictor Variables before fitting them into my model | How to test and avoid multicollinearity in mixed linear model?
For VIF calculation usdm can also be package ( I need to install "usdm")
library(usdm)
df = # Data Frame
vif(df)
If VIF > 4.0 then I generally assume multicollinearity remove all those Predictor Var |
8,672 | How to test and avoid multicollinearity in mixed linear model? | An update, since I found this question useful but can't add comments -
The code from Zuur et al. (2009) is also available via the supplementary material to a subsequent (and very useful) publication of their's in the journal Methods in Ecology and Evolution.
The paper - A protocol for data exploration to avoid common ... | How to test and avoid multicollinearity in mixed linear model? | An update, since I found this question useful but can't add comments -
The code from Zuur et al. (2009) is also available via the supplementary material to a subsequent (and very useful) publication | How to test and avoid multicollinearity in mixed linear model?
An update, since I found this question useful but can't add comments -
The code from Zuur et al. (2009) is also available via the supplementary material to a subsequent (and very useful) publication of their's in the journal Methods in Ecology and Evolutio... | How to test and avoid multicollinearity in mixed linear model?
An update, since I found this question useful but can't add comments -
The code from Zuur et al. (2009) is also available via the supplementary material to a subsequent (and very useful) publication |
8,673 | How to test and avoid multicollinearity in mixed linear model? | VIF (variance inflation factor) can be measured simply by:
library(car)
vif(yourmodel) #this should work for lme4::lmer mixed models. | How to test and avoid multicollinearity in mixed linear model? | VIF (variance inflation factor) can be measured simply by:
library(car)
vif(yourmodel) #this should work for lme4::lmer mixed models. | How to test and avoid multicollinearity in mixed linear model?
VIF (variance inflation factor) can be measured simply by:
library(car)
vif(yourmodel) #this should work for lme4::lmer mixed models. | How to test and avoid multicollinearity in mixed linear model?
VIF (variance inflation factor) can be measured simply by:
library(car)
vif(yourmodel) #this should work for lme4::lmer mixed models. |
8,674 | How to test and avoid multicollinearity in mixed linear model? | Maybe qr() function will work. If X is your data frame or matrix, you can use qr(X)$pivot. For example, qr(X)$pivot= c(1, 2, 4, 5, 7, 8, 3, 6), then column 3 and 6 is the multicollinear variable. | How to test and avoid multicollinearity in mixed linear model? | Maybe qr() function will work. If X is your data frame or matrix, you can use qr(X)$pivot. For example, qr(X)$pivot= c(1, 2, 4, 5, 7, 8, 3, 6), then column 3 and 6 is the multicollinear variable. | How to test and avoid multicollinearity in mixed linear model?
Maybe qr() function will work. If X is your data frame or matrix, you can use qr(X)$pivot. For example, qr(X)$pivot= c(1, 2, 4, 5, 7, 8, 3, 6), then column 3 and 6 is the multicollinear variable. | How to test and avoid multicollinearity in mixed linear model?
Maybe qr() function will work. If X is your data frame or matrix, you can use qr(X)$pivot. For example, qr(X)$pivot= c(1, 2, 4, 5, 7, 8, 3, 6), then column 3 and 6 is the multicollinear variable. |
8,675 | How to test and avoid multicollinearity in mixed linear model? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
To assess multicollinearity between predictors when ru... | How to test and avoid multicollinearity in mixed linear model? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| How to test and avoid multicollinearity in mixed linear model?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | How to test and avoid multicollinearity in mixed linear model?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
8,676 | What is the reason that the Adam Optimizer is considered robust to the value of its hyper parameters? | In regards to the evidence in regards to the claim, I believe the only evidence supporting the claim can be found on figure 4 in their paper. They show the final results under a range of different values for $\beta_1$, $\beta_2$ and $\alpha$.
Personally, I don't find their argument convincing, in particular because th... | What is the reason that the Adam Optimizer is considered robust to the value of its hyper parameters | In regards to the evidence in regards to the claim, I believe the only evidence supporting the claim can be found on figure 4 in their paper. They show the final results under a range of different val | What is the reason that the Adam Optimizer is considered robust to the value of its hyper parameters?
In regards to the evidence in regards to the claim, I believe the only evidence supporting the claim can be found on figure 4 in their paper. They show the final results under a range of different values for $\beta_1$,... | What is the reason that the Adam Optimizer is considered robust to the value of its hyper parameters
In regards to the evidence in regards to the claim, I believe the only evidence supporting the claim can be found on figure 4 in their paper. They show the final results under a range of different val |
8,677 | What is the reason that the Adam Optimizer is considered robust to the value of its hyper parameters? | Adam learns the learning rates itself, on a per-parameter basis. The parameters $\beta_1$ and $\beta_2$ don't directly define the learning rate, just the timescales over which the learned learning rates decay. If they decay really fast, then the learning rates will jump about all over the place. If they decay slowly, ... | What is the reason that the Adam Optimizer is considered robust to the value of its hyper parameters | Adam learns the learning rates itself, on a per-parameter basis. The parameters $\beta_1$ and $\beta_2$ don't directly define the learning rate, just the timescales over which the learned learning rat | What is the reason that the Adam Optimizer is considered robust to the value of its hyper parameters?
Adam learns the learning rates itself, on a per-parameter basis. The parameters $\beta_1$ and $\beta_2$ don't directly define the learning rate, just the timescales over which the learned learning rates decay. If they... | What is the reason that the Adam Optimizer is considered robust to the value of its hyper parameters
Adam learns the learning rates itself, on a per-parameter basis. The parameters $\beta_1$ and $\beta_2$ don't directly define the learning rate, just the timescales over which the learned learning rat |
8,678 | How should an individual researcher think about the false discovery rate? | In order to aggregate the results of multiple studies you should rather think of making your results accessible for meta analyses. A meta analysis considers the data of the study, or at least its estimates, models study effects and comes to a systematical conclusion by forming some kind of large virtual study out of ma... | How should an individual researcher think about the false discovery rate? | In order to aggregate the results of multiple studies you should rather think of making your results accessible for meta analyses. A meta analysis considers the data of the study, or at least its esti | How should an individual researcher think about the false discovery rate?
In order to aggregate the results of multiple studies you should rather think of making your results accessible for meta analyses. A meta analysis considers the data of the study, or at least its estimates, models study effects and comes to a sys... | How should an individual researcher think about the false discovery rate?
In order to aggregate the results of multiple studies you should rather think of making your results accessible for meta analyses. A meta analysis considers the data of the study, or at least its esti |
8,679 | How should an individual researcher think about the false discovery rate? | If I [individual researcher] have a guess of what the size of the effect I'm studying should be [...], should I adjust my $\alpha$ level until the FDR = .05? Should I publish results at the $\alpha=.05$ level even if my studies are underpowered and leave consideration of the FDR to consumers of the literature?
I would... | How should an individual researcher think about the false discovery rate? | If I [individual researcher] have a guess of what the size of the effect I'm studying should be [...], should I adjust my $\alpha$ level until the FDR = .05? Should I publish results at the $\alpha=.0 | How should an individual researcher think about the false discovery rate?
If I [individual researcher] have a guess of what the size of the effect I'm studying should be [...], should I adjust my $\alpha$ level until the FDR = .05? Should I publish results at the $\alpha=.05$ level even if my studies are underpowered a... | How should an individual researcher think about the false discovery rate?
If I [individual researcher] have a guess of what the size of the effect I'm studying should be [...], should I adjust my $\alpha$ level until the FDR = .05? Should I publish results at the $\alpha=.0 |
8,680 | How should an individual researcher think about the false discovery rate? | This is actually a deep philosophical question. I'm a researcher myself and I've thought a while about this. But before an answer, let's review exactly what the false discovery rate is.
FDR versus P
P is simply a measure of the probability of saying that there is a difference, when there is no difference at all and doe... | How should an individual researcher think about the false discovery rate? | This is actually a deep philosophical question. I'm a researcher myself and I've thought a while about this. But before an answer, let's review exactly what the false discovery rate is.
FDR versus P
P | How should an individual researcher think about the false discovery rate?
This is actually a deep philosophical question. I'm a researcher myself and I've thought a while about this. But before an answer, let's review exactly what the false discovery rate is.
FDR versus P
P is simply a measure of the probability of say... | How should an individual researcher think about the false discovery rate?
This is actually a deep philosophical question. I'm a researcher myself and I've thought a while about this. But before an answer, let's review exactly what the false discovery rate is.
FDR versus P
P |
8,681 | How should an individual researcher think about the false discovery rate? | To help understand the relationships, I created this graph of FDR as a function of prior probability for various powers (with alpha=0.05). Note this graph, and the equation of @Buckminster computes the FDR for all results with P less than alpha. The graph would look different if you only considered P values very close ... | How should an individual researcher think about the false discovery rate? | To help understand the relationships, I created this graph of FDR as a function of prior probability for various powers (with alpha=0.05). Note this graph, and the equation of @Buckminster computes th | How should an individual researcher think about the false discovery rate?
To help understand the relationships, I created this graph of FDR as a function of prior probability for various powers (with alpha=0.05). Note this graph, and the equation of @Buckminster computes the FDR for all results with P less than alpha. ... | How should an individual researcher think about the false discovery rate?
To help understand the relationships, I created this graph of FDR as a function of prior probability for various powers (with alpha=0.05). Note this graph, and the equation of @Buckminster computes th |
8,682 | How should an individual researcher think about the false discovery rate? | To suggest publication is a decision. I think that it is worthwhile to study what are benefits and costs associated with this decision.
1) Academic environment universally pushes researchers to publish more, thought various rankings of publications will affect also this record. We can presume that more prestigious jo... | How should an individual researcher think about the false discovery rate? | To suggest publication is a decision. I think that it is worthwhile to study what are benefits and costs associated with this decision.
1) Academic environment universally pushes researchers to publ | How should an individual researcher think about the false discovery rate?
To suggest publication is a decision. I think that it is worthwhile to study what are benefits and costs associated with this decision.
1) Academic environment universally pushes researchers to publish more, thought various rankings of publicat... | How should an individual researcher think about the false discovery rate?
To suggest publication is a decision. I think that it is worthwhile to study what are benefits and costs associated with this decision.
1) Academic environment universally pushes researchers to publ |
8,683 | How to define the termination condition for gradient descent? | Nice question. I've seen lots of stopping rules in the literature, and there are advantages and disadvantages to each, depending on context. The optim function in R, for example, has at least three different stopping rules:
maxit, i.e. a predetermined maximum number of iterations. Another similar alternative I've see... | How to define the termination condition for gradient descent? | Nice question. I've seen lots of stopping rules in the literature, and there are advantages and disadvantages to each, depending on context. The optim function in R, for example, has at least three d | How to define the termination condition for gradient descent?
Nice question. I've seen lots of stopping rules in the literature, and there are advantages and disadvantages to each, depending on context. The optim function in R, for example, has at least three different stopping rules:
maxit, i.e. a predetermined maxi... | How to define the termination condition for gradient descent?
Nice question. I've seen lots of stopping rules in the literature, and there are advantages and disadvantages to each, depending on context. The optim function in R, for example, has at least three d |
8,684 | 0-1 Loss Function explanation | You have correctly summarized the 0-1 loss function as effectively looking at accuracy. Your 1's become indicators for misclassified items, regardless of how they were misclassified. Since you have three 1's out of 10 items, your classification accuracy is 70%.
If you change the weighting on the loss function, this int... | 0-1 Loss Function explanation | You have correctly summarized the 0-1 loss function as effectively looking at accuracy. Your 1's become indicators for misclassified items, regardless of how they were misclassified. Since you have th | 0-1 Loss Function explanation
You have correctly summarized the 0-1 loss function as effectively looking at accuracy. Your 1's become indicators for misclassified items, regardless of how they were misclassified. Since you have three 1's out of 10 items, your classification accuracy is 70%.
If you change the weighting ... | 0-1 Loss Function explanation
You have correctly summarized the 0-1 loss function as effectively looking at accuracy. Your 1's become indicators for misclassified items, regardless of how they were misclassified. Since you have th |
8,685 | 0-1 Loss Function explanation | Yes, this is basically it: you count the number of misclassified items. There is nothing more behind it, it is a very basic loss function. What follows, 0-1 loss leads to estimating mode of the target distribution (as compared to $L_1$ loss for estimating median and $L_2$ loss for estimating mean). | 0-1 Loss Function explanation | Yes, this is basically it: you count the number of misclassified items. There is nothing more behind it, it is a very basic loss function. What follows, 0-1 loss leads to estimating mode of the target | 0-1 Loss Function explanation
Yes, this is basically it: you count the number of misclassified items. There is nothing more behind it, it is a very basic loss function. What follows, 0-1 loss leads to estimating mode of the target distribution (as compared to $L_1$ loss for estimating median and $L_2$ loss for estimati... | 0-1 Loss Function explanation
Yes, this is basically it: you count the number of misclassified items. There is nothing more behind it, it is a very basic loss function. What follows, 0-1 loss leads to estimating mode of the target |
8,686 | 0-1 Loss Function explanation | I think your confusion is not differentiating the loss for one data point vs. the loss for the whole data set.
Specifically, your $L(y,\hat y)$ is the loss for one data point (I am changing the notation little bit). And the loss for the whole data set, i.e., classification accuracy, needs to summing all data points.
$$... | 0-1 Loss Function explanation | I think your confusion is not differentiating the loss for one data point vs. the loss for the whole data set.
Specifically, your $L(y,\hat y)$ is the loss for one data point (I am changing the notati | 0-1 Loss Function explanation
I think your confusion is not differentiating the loss for one data point vs. the loss for the whole data set.
Specifically, your $L(y,\hat y)$ is the loss for one data point (I am changing the notation little bit). And the loss for the whole data set, i.e., classification accuracy, needs ... | 0-1 Loss Function explanation
I think your confusion is not differentiating the loss for one data point vs. the loss for the whole data set.
Specifically, your $L(y,\hat y)$ is the loss for one data point (I am changing the notati |
8,687 | If the Epanechnikov kernel is theoretically optimal when doing Kernel Density Estimation, why isn't it more commonly used? | The reason why the Epanechnikov kernel isn't universally used for its theoretical optimality may very well be that the Epanechnikov kernel isn't actually theoretically optimal. Tsybakov explicitly criticizes the argument that the Epanechnikov kernel is "theoretically optimal" in pp. 16-19 of Introduction to Nonparametr... | If the Epanechnikov kernel is theoretically optimal when doing Kernel Density Estimation, why isn't | The reason why the Epanechnikov kernel isn't universally used for its theoretical optimality may very well be that the Epanechnikov kernel isn't actually theoretically optimal. Tsybakov explicitly cri | If the Epanechnikov kernel is theoretically optimal when doing Kernel Density Estimation, why isn't it more commonly used?
The reason why the Epanechnikov kernel isn't universally used for its theoretical optimality may very well be that the Epanechnikov kernel isn't actually theoretically optimal. Tsybakov explicitly ... | If the Epanechnikov kernel is theoretically optimal when doing Kernel Density Estimation, why isn't
The reason why the Epanechnikov kernel isn't universally used for its theoretical optimality may very well be that the Epanechnikov kernel isn't actually theoretically optimal. Tsybakov explicitly cri |
8,688 | If the Epanechnikov kernel is theoretically optimal when doing Kernel Density Estimation, why isn't it more commonly used? | The Gaussian kernel is used for example in density estimation through derivatives:
$$\frac{d^if}{dx^i}(x)\approx \frac{1}{bandwidth}\sum_{j=1}^N \frac{d^ik}{dx^i}(X_j,x)$$
This is because the Epanechnikov kernel has 3 derivatives before it's identically zero, unlike the Gaussian which has infinitely many (nonzero) deri... | If the Epanechnikov kernel is theoretically optimal when doing Kernel Density Estimation, why isn't | The Gaussian kernel is used for example in density estimation through derivatives:
$$\frac{d^if}{dx^i}(x)\approx \frac{1}{bandwidth}\sum_{j=1}^N \frac{d^ik}{dx^i}(X_j,x)$$
This is because the Epanechn | If the Epanechnikov kernel is theoretically optimal when doing Kernel Density Estimation, why isn't it more commonly used?
The Gaussian kernel is used for example in density estimation through derivatives:
$$\frac{d^if}{dx^i}(x)\approx \frac{1}{bandwidth}\sum_{j=1}^N \frac{d^ik}{dx^i}(X_j,x)$$
This is because the Epane... | If the Epanechnikov kernel is theoretically optimal when doing Kernel Density Estimation, why isn't
The Gaussian kernel is used for example in density estimation through derivatives:
$$\frac{d^if}{dx^i}(x)\approx \frac{1}{bandwidth}\sum_{j=1}^N \frac{d^ik}{dx^i}(X_j,x)$$
This is because the Epanechn |
8,689 | What does "independent observations" mean? | In probability theory, statistical independence (which is not the same as causal independence) is defined as your property (3), but (1) follows as a consequence$\dagger$. The events $\mathcal{A}$ and $\mathcal{B}$ are said to be statistically independent if and only if:
$$\mathbb{P}(\mathcal{A} \cap \mathcal{B}) = \ma... | What does "independent observations" mean? | In probability theory, statistical independence (which is not the same as causal independence) is defined as your property (3), but (1) follows as a consequence$\dagger$. The events $\mathcal{A}$ and | What does "independent observations" mean?
In probability theory, statistical independence (which is not the same as causal independence) is defined as your property (3), but (1) follows as a consequence$\dagger$. The events $\mathcal{A}$ and $\mathcal{B}$ are said to be statistically independent if and only if:
$$\ma... | What does "independent observations" mean?
In probability theory, statistical independence (which is not the same as causal independence) is defined as your property (3), but (1) follows as a consequence$\dagger$. The events $\mathcal{A}$ and |
8,690 | What does "independent observations" mean? | The definitions of statistical independence that you give in your post are all essentially correct, but they don't get to the heart of the independence assumption in a statistical model. To understand what we mean by the assumption of independent observations in a statistical model, it will be helpful to revisit what ... | What does "independent observations" mean? | The definitions of statistical independence that you give in your post are all essentially correct, but they don't get to the heart of the independence assumption in a statistical model. To understan | What does "independent observations" mean?
The definitions of statistical independence that you give in your post are all essentially correct, but they don't get to the heart of the independence assumption in a statistical model. To understand what we mean by the assumption of independent observations in a statistical... | What does "independent observations" mean?
The definitions of statistical independence that you give in your post are all essentially correct, but they don't get to the heart of the independence assumption in a statistical model. To understan |
8,691 | What does "independent observations" mean? | Let $\mathbb x=(X_1,...,X_j,...,X_k)$ by a $k-$dimensional random vector, i.e. a fixed-position collection of random variables (measurable real functions).
Consider many such vectors, say $n$, and index these vectors by $i=1,...,n$, so, say
$$\mathbb x_i=(X_{1i},...,X_{ji},...,X_{ki})$$ and regard them as a collection... | What does "independent observations" mean? | Let $\mathbb x=(X_1,...,X_j,...,X_k)$ by a $k-$dimensional random vector, i.e. a fixed-position collection of random variables (measurable real functions).
Consider many such vectors, say $n$, and in | What does "independent observations" mean?
Let $\mathbb x=(X_1,...,X_j,...,X_k)$ by a $k-$dimensional random vector, i.e. a fixed-position collection of random variables (measurable real functions).
Consider many such vectors, say $n$, and index these vectors by $i=1,...,n$, so, say
$$\mathbb x_i=(X_{1i},...,X_{ji},..... | What does "independent observations" mean?
Let $\mathbb x=(X_1,...,X_j,...,X_k)$ by a $k-$dimensional random vector, i.e. a fixed-position collection of random variables (measurable real functions).
Consider many such vectors, say $n$, and in |
8,692 | Generate two variables with precise pre-specified correlation [duplicate] | For R you can use the mvrnorm function in the MASS package and set empirical=TRUE.
Or this post shows the steps in R for creating new variables with specific correlations to an existing variable. | Generate two variables with precise pre-specified correlation [duplicate] | For R you can use the mvrnorm function in the MASS package and set empirical=TRUE.
Or this post shows the steps in R for creating new variables with specific correlations to an existing variable. | Generate two variables with precise pre-specified correlation [duplicate]
For R you can use the mvrnorm function in the MASS package and set empirical=TRUE.
Or this post shows the steps in R for creating new variables with specific correlations to an existing variable. | Generate two variables with precise pre-specified correlation [duplicate]
For R you can use the mvrnorm function in the MASS package and set empirical=TRUE.
Or this post shows the steps in R for creating new variables with specific correlations to an existing variable. |
8,693 | Who invented k-fold cross-validation? | One paper that might be worth consulting is
Stone M. Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc., 36(2), 111β147, 1974.
I have seen references to
Mosteller F. and Tukey J.W. Data analysis, including statistics. In Handbook of Social Psychology. Addison-Wesley, Reading, MA, 19... | Who invented k-fold cross-validation? | One paper that might be worth consulting is
Stone M. Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc., 36(2), 111β147, 1974.
I have seen references to
Mosteller | Who invented k-fold cross-validation?
One paper that might be worth consulting is
Stone M. Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc., 36(2), 111β147, 1974.
I have seen references to
Mosteller F. and Tukey J.W. Data analysis, including statistics. In Handbook of Social Psych... | Who invented k-fold cross-validation?
One paper that might be worth consulting is
Stone M. Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc., 36(2), 111β147, 1974.
I have seen references to
Mosteller |
8,694 | Clustering procedure where each cluster has an equal number of points? | I suggest a two-step approach:
get a good initial estimates of the cluster centers, e.g. using hard or fuzzy K-means.
Use Global Nearest Neighbor assignment to associate points with cluster centers: Calculate a distance matrix between each point and each cluster center (you can make the problem a bit smaller by only... | Clustering procedure where each cluster has an equal number of points? | I suggest a two-step approach:
get a good initial estimates of the cluster centers, e.g. using hard or fuzzy K-means.
Use Global Nearest Neighbor assignment to associate points with cluster centers | Clustering procedure where each cluster has an equal number of points?
I suggest a two-step approach:
get a good initial estimates of the cluster centers, e.g. using hard or fuzzy K-means.
Use Global Nearest Neighbor assignment to associate points with cluster centers: Calculate a distance matrix between each point ... | Clustering procedure where each cluster has an equal number of points?
I suggest a two-step approach:
get a good initial estimates of the cluster centers, e.g. using hard or fuzzy K-means.
Use Global Nearest Neighbor assignment to associate points with cluster centers |
8,695 | Clustering procedure where each cluster has an equal number of points? | Try this k-means variation:
Initialization:
choose k centers from the dataset at random, or even better using kmeans++ strategy
for each point, compute the distance to its nearest cluster center, and build a heap for this
draw points from the heap, and assign them to the nearest cluster, unless the cluster is already ... | Clustering procedure where each cluster has an equal number of points? | Try this k-means variation:
Initialization:
choose k centers from the dataset at random, or even better using kmeans++ strategy
for each point, compute the distance to its nearest cluster center, and | Clustering procedure where each cluster has an equal number of points?
Try this k-means variation:
Initialization:
choose k centers from the dataset at random, or even better using kmeans++ strategy
for each point, compute the distance to its nearest cluster center, and build a heap for this
draw points from the heap,... | Clustering procedure where each cluster has an equal number of points?
Try this k-means variation:
Initialization:
choose k centers from the dataset at random, or even better using kmeans++ strategy
for each point, compute the distance to its nearest cluster center, and |
8,696 | Clustering procedure where each cluster has an equal number of points? | Recently I needed this myself for a not very large dataset. My answer, although it has a relatively long running time, is guaranteed to converge to a local optimum.
def eqsc(X, K=None, G=None):
"equal-size clustering based on data exchanges between pairs of clusters"
from scipy.spatial.distance import pdist, sq... | Clustering procedure where each cluster has an equal number of points? | Recently I needed this myself for a not very large dataset. My answer, although it has a relatively long running time, is guaranteed to converge to a local optimum.
def eqsc(X, K=None, G=None):
"e | Clustering procedure where each cluster has an equal number of points?
Recently I needed this myself for a not very large dataset. My answer, although it has a relatively long running time, is guaranteed to converge to a local optimum.
def eqsc(X, K=None, G=None):
"equal-size clustering based on data exchanges betw... | Clustering procedure where each cluster has an equal number of points?
Recently I needed this myself for a not very large dataset. My answer, although it has a relatively long running time, is guaranteed to converge to a local optimum.
def eqsc(X, K=None, G=None):
"e |
8,697 | Clustering procedure where each cluster has an equal number of points? | This is an optimisation problem. We have an open source java library which solves this problem (clustering where quantity per cluster must be between set ranges). You'd need your total number of points to be maximum of a few thousand though - no more than 5000 or maybe 10000.
The library is here:
https://github.com/PG... | Clustering procedure where each cluster has an equal number of points? | This is an optimisation problem. We have an open source java library which solves this problem (clustering where quantity per cluster must be between set ranges). You'd need your total number of point | Clustering procedure where each cluster has an equal number of points?
This is an optimisation problem. We have an open source java library which solves this problem (clustering where quantity per cluster must be between set ranges). You'd need your total number of points to be maximum of a few thousand though - no mor... | Clustering procedure where each cluster has an equal number of points?
This is an optimisation problem. We have an open source java library which solves this problem (clustering where quantity per cluster must be between set ranges). You'd need your total number of point |
8,698 | Clustering procedure where each cluster has an equal number of points? | I suggest the recent paper Discriminative Clustering by Regularized Information Maximization (and references therein). Specifically, Section 2 talks about class balance and cluster assumption. | Clustering procedure where each cluster has an equal number of points? | I suggest the recent paper Discriminative Clustering by Regularized Information Maximization (and references therein). Specifically, Section 2 talks about class balance and cluster assumption. | Clustering procedure where each cluster has an equal number of points?
I suggest the recent paper Discriminative Clustering by Regularized Information Maximization (and references therein). Specifically, Section 2 talks about class balance and cluster assumption. | Clustering procedure where each cluster has an equal number of points?
I suggest the recent paper Discriminative Clustering by Regularized Information Maximization (and references therein). Specifically, Section 2 talks about class balance and cluster assumption. |
8,699 | StackExchange fires a moderator, and now in response hundreds of moderators resign: is the increase in resignations statistically significant? | This is an interesting investigation because of the flash-pan nature of the event. It's not the same as, say, installing a fence and trying to see if the number of trespassers was reduced. In that case, after the fence was installed, we would expect to see a permanent impact (if there was any) on the rate of trespasser... | StackExchange fires a moderator, and now in response hundreds of moderators resign: is the increase | This is an interesting investigation because of the flash-pan nature of the event. It's not the same as, say, installing a fence and trying to see if the number of trespassers was reduced. In that cas | StackExchange fires a moderator, and now in response hundreds of moderators resign: is the increase in resignations statistically significant?
This is an interesting investigation because of the flash-pan nature of the event. It's not the same as, say, installing a fence and trying to see if the number of trespassers w... | StackExchange fires a moderator, and now in response hundreds of moderators resign: is the increase
This is an interesting investigation because of the flash-pan nature of the event. It's not the same as, say, installing a fence and trying to see if the number of trespassers was reduced. In that cas |
8,700 | StackExchange fires a moderator, and now in response hundreds of moderators resign: is the increase in resignations statistically significant? | The analysis you are proposing sounds interesting, but the data collection process will be quite complicated. There are a few main issues you are going to have to deal with:
Determine the scope of events of interest: Ideally you should determine the scope of events of interest to you (even in just a broad way) before... | StackExchange fires a moderator, and now in response hundreds of moderators resign: is the increase | The analysis you are proposing sounds interesting, but the data collection process will be quite complicated. There are a few main issues you are going to have to deal with:
Determine the scope of e | StackExchange fires a moderator, and now in response hundreds of moderators resign: is the increase in resignations statistically significant?
The analysis you are proposing sounds interesting, but the data collection process will be quite complicated. There are a few main issues you are going to have to deal with:
D... | StackExchange fires a moderator, and now in response hundreds of moderators resign: is the increase
The analysis you are proposing sounds interesting, but the data collection process will be quite complicated. There are a few main issues you are going to have to deal with:
Determine the scope of e |
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