idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
|---|---|---|---|---|---|---|
42,601 | Is there a "pure R" implementation for loess? (with no C code?) | The loess.demo function in the TeachingDemos package replicates some of the internals in plain R (it also uses the built in C code version). You could use that function as a starting place depending on what you want to do. | Is there a "pure R" implementation for loess? (with no C code?) | The loess.demo function in the TeachingDemos package replicates some of the internals in plain R (it also uses the built in C code version). You could use that function as a starting place depending | Is there a "pure R" implementation for loess? (with no C code?)
The loess.demo function in the TeachingDemos package replicates some of the internals in plain R (it also uses the built in C code version). You could use that function as a starting place depending on what you want to do. | Is there a "pure R" implementation for loess? (with no C code?)
The loess.demo function in the TeachingDemos package replicates some of the internals in plain R (it also uses the built in C code version). You could use that function as a starting place depending |
42,602 | Off-diagonal range for guaranteed positive definiteness? | The maximum is $1/(p-1)$.
To see this, note first that the eigenvalues of the matrix with all off-diagonal entries equal to a constant $x$ are $1-x$ (with multiplicity $p-1$) and $1+(p-1)x$. When $x \lt -1/(p-1)$, the smallest eigenvalue will therefore be negative implying the matrix is not positive definite. Because... | Off-diagonal range for guaranteed positive definiteness? | The maximum is $1/(p-1)$.
To see this, note first that the eigenvalues of the matrix with all off-diagonal entries equal to a constant $x$ are $1-x$ (with multiplicity $p-1$) and $1+(p-1)x$. When $x | Off-diagonal range for guaranteed positive definiteness?
The maximum is $1/(p-1)$.
To see this, note first that the eigenvalues of the matrix with all off-diagonal entries equal to a constant $x$ are $1-x$ (with multiplicity $p-1$) and $1+(p-1)x$. When $x \lt -1/(p-1)$, the smallest eigenvalue will therefore be negati... | Off-diagonal range for guaranteed positive definiteness?
The maximum is $1/(p-1)$.
To see this, note first that the eigenvalues of the matrix with all off-diagonal entries equal to a constant $x$ are $1-x$ (with multiplicity $p-1$) and $1+(p-1)x$. When $x |
42,603 | Outlier detection for generic time series | You are quite right that the ARIMA Model you are using (first differences) may not be appropriate to detect outliers. Outliers can be Pulses, Level Shifts, Seasonal Pulses or Local Time Trends. You might want to google "INTERVENTION DETECTION IN TIME SERIES" or google "AUTOMATIC INTERVENTION DETECTION" to get some read... | Outlier detection for generic time series | You are quite right that the ARIMA Model you are using (first differences) may not be appropriate to detect outliers. Outliers can be Pulses, Level Shifts, Seasonal Pulses or Local Time Trends. You mi | Outlier detection for generic time series
You are quite right that the ARIMA Model you are using (first differences) may not be appropriate to detect outliers. Outliers can be Pulses, Level Shifts, Seasonal Pulses or Local Time Trends. You might want to google "INTERVENTION DETECTION IN TIME SERIES" or google "AUTOMATI... | Outlier detection for generic time series
You are quite right that the ARIMA Model you are using (first differences) may not be appropriate to detect outliers. Outliers can be Pulses, Level Shifts, Seasonal Pulses or Local Time Trends. You mi |
42,604 | Outlier detection for generic time series | Winsorization replaces extreme data values with less extreme values.
http://www.r-bloggers.com/winsorization/ | Outlier detection for generic time series | Winsorization replaces extreme data values with less extreme values.
http://www.r-bloggers.com/winsorization/ | Outlier detection for generic time series
Winsorization replaces extreme data values with less extreme values.
http://www.r-bloggers.com/winsorization/ | Outlier detection for generic time series
Winsorization replaces extreme data values with less extreme values.
http://www.r-bloggers.com/winsorization/ |
42,605 | What is the difference between hazard and crude ratio? | Actually, it sounds like the hazard ratio you obtained was the adjusted ratio, whereas by definition the crude ratio has no adjustment for covariates.
When you did your multivariable Cox regression, you included some covariates in the model, and the crude ratio would be the hazard ratio without these covariates include... | What is the difference between hazard and crude ratio? | Actually, it sounds like the hazard ratio you obtained was the adjusted ratio, whereas by definition the crude ratio has no adjustment for covariates.
When you did your multivariable Cox regression, y | What is the difference between hazard and crude ratio?
Actually, it sounds like the hazard ratio you obtained was the adjusted ratio, whereas by definition the crude ratio has no adjustment for covariates.
When you did your multivariable Cox regression, you included some covariates in the model, and the crude ratio wou... | What is the difference between hazard and crude ratio?
Actually, it sounds like the hazard ratio you obtained was the adjusted ratio, whereas by definition the crude ratio has no adjustment for covariates.
When you did your multivariable Cox regression, y |
42,606 | Is it necessary to perform a transformation on proportion data if it's reasonably well behaved? | It depends. If your goal is prediction, then you may not need to do any gymnastics to get a more theoretically sound model if the one in hand does well. But of course you should be always be aware that a good-fitting model to present data may not perform well on new data. You can try to get a feel for that using cross ... | Is it necessary to perform a transformation on proportion data if it's reasonably well behaved? | It depends. If your goal is prediction, then you may not need to do any gymnastics to get a more theoretically sound model if the one in hand does well. But of course you should be always be aware tha | Is it necessary to perform a transformation on proportion data if it's reasonably well behaved?
It depends. If your goal is prediction, then you may not need to do any gymnastics to get a more theoretically sound model if the one in hand does well. But of course you should be always be aware that a good-fitting model t... | Is it necessary to perform a transformation on proportion data if it's reasonably well behaved?
It depends. If your goal is prediction, then you may not need to do any gymnastics to get a more theoretically sound model if the one in hand does well. But of course you should be always be aware tha |
42,607 | How do I ensure PROC ARIMA is performing the correct parameterization of input variables? | Specifying the Input Variables' ARIMA Models
The ARIMA Procedure uses the results of the first pair(s) of identify and estimate statements (i.e., the identify and estimate statements for the input variables) to create models to forecast the values of the input variable(s) (also called exogenous variable(s)) after the l... | How do I ensure PROC ARIMA is performing the correct parameterization of input variables? | Specifying the Input Variables' ARIMA Models
The ARIMA Procedure uses the results of the first pair(s) of identify and estimate statements (i.e., the identify and estimate statements for the input var | How do I ensure PROC ARIMA is performing the correct parameterization of input variables?
Specifying the Input Variables' ARIMA Models
The ARIMA Procedure uses the results of the first pair(s) of identify and estimate statements (i.e., the identify and estimate statements for the input variables) to create models to fo... | How do I ensure PROC ARIMA is performing the correct parameterization of input variables?
Specifying the Input Variables' ARIMA Models
The ARIMA Procedure uses the results of the first pair(s) of identify and estimate statements (i.e., the identify and estimate statements for the input var |
42,608 | How do I ensure PROC ARIMA is performing the correct parameterization of input variables? | I have reviewed the output and the forecast refelects an AR(12) in the error term which translates to a 12 period weighted forecast using the last 12 values of both your predictor series as the AR polynomial acts a multiplier across all series ( X,Y,Z ). Without getting into it in great detail , your model specificatio... | How do I ensure PROC ARIMA is performing the correct parameterization of input variables? | I have reviewed the output and the forecast refelects an AR(12) in the error term which translates to a 12 period weighted forecast using the last 12 values of both your predictor series as the AR pol | How do I ensure PROC ARIMA is performing the correct parameterization of input variables?
I have reviewed the output and the forecast refelects an AR(12) in the error term which translates to a 12 period weighted forecast using the last 12 values of both your predictor series as the AR polynomial acts a multiplier acro... | How do I ensure PROC ARIMA is performing the correct parameterization of input variables?
I have reviewed the output and the forecast refelects an AR(12) in the error term which translates to a 12 period weighted forecast using the last 12 values of both your predictor series as the AR pol |
42,609 | How to test if the mean equals the median? | This is a bootstrap confidence interval for the (median - mean) difference in R:
z = function() {s = sample(women$weight, replace=TRUE); median(s)-mean(s)}
k = replicate(10000, z())
c(quantile(k, c(.025, .5, .975)), mean=mean(k), sd=sd(k), qgte0=mean(k>=0))
2.5% 50% 97.5% mean sd qgte0
... | How to test if the mean equals the median? | This is a bootstrap confidence interval for the (median - mean) difference in R:
z = function() {s = sample(women$weight, replace=TRUE); median(s)-mean(s)}
k = replicate(10000, z())
c(quantile(k, c(.0 | How to test if the mean equals the median?
This is a bootstrap confidence interval for the (median - mean) difference in R:
z = function() {s = sample(women$weight, replace=TRUE); median(s)-mean(s)}
k = replicate(10000, z())
c(quantile(k, c(.025, .5, .975)), mean=mean(k), sd=sd(k), qgte0=mean(k>=0))
2.5% 50... | How to test if the mean equals the median?
This is a bootstrap confidence interval for the (median - mean) difference in R:
z = function() {s = sample(women$weight, replace=TRUE); median(s)-mean(s)}
k = replicate(10000, z())
c(quantile(k, c(.0 |
42,610 | How to test if the mean equals the median? | A permutations test can easily be set up to use the (mean - mode difference) as its test statistic. That would give you an exact P value for the difference. | How to test if the mean equals the median? | A permutations test can easily be set up to use the (mean - mode difference) as its test statistic. That would give you an exact P value for the difference. | How to test if the mean equals the median?
A permutations test can easily be set up to use the (mean - mode difference) as its test statistic. That would give you an exact P value for the difference. | How to test if the mean equals the median?
A permutations test can easily be set up to use the (mean - mode difference) as its test statistic. That would give you an exact P value for the difference. |
42,611 | How to check for bivariate Gaussianity without the use of regression? | I have recently come across this method that was displayed in Johnson and Wichern.
Let the data points that you want to test for bivariate normality be designated as $\{ x_{i} \}$. Next, compute the sample covariance matrix and deisgnate it as $S$.
For each observed point calculate $d_{j}^{2} = (x_{j} - \bar{x})^{T} S^... | How to check for bivariate Gaussianity without the use of regression? | I have recently come across this method that was displayed in Johnson and Wichern.
Let the data points that you want to test for bivariate normality be designated as $\{ x_{i} \}$. Next, compute the s | How to check for bivariate Gaussianity without the use of regression?
I have recently come across this method that was displayed in Johnson and Wichern.
Let the data points that you want to test for bivariate normality be designated as $\{ x_{i} \}$. Next, compute the sample covariance matrix and deisgnate it as $S$.
F... | How to check for bivariate Gaussianity without the use of regression?
I have recently come across this method that was displayed in Johnson and Wichern.
Let the data points that you want to test for bivariate normality be designated as $\{ x_{i} \}$. Next, compute the s |
42,612 | Operations on probability distributions of continuous random variables | If you have a random variable $X$ distributed with continuous distribution function $F$, and you define a random variable $Y=h(X)$, then what is its distribution function? Let's just use the definition of distribution function:
\begin{align}
G(y) &= P\{Y \le y\} \\
&= P\{h(X) \le y\}
\end{align}
If $h$ is monoton... | Operations on probability distributions of continuous random variables | If you have a random variable $X$ distributed with continuous distribution function $F$, and you define a random variable $Y=h(X)$, then what is its distribution function? Let's just use the definiti | Operations on probability distributions of continuous random variables
If you have a random variable $X$ distributed with continuous distribution function $F$, and you define a random variable $Y=h(X)$, then what is its distribution function? Let's just use the definition of distribution function:
\begin{align}
G(y) &... | Operations on probability distributions of continuous random variables
If you have a random variable $X$ distributed with continuous distribution function $F$, and you define a random variable $Y=h(X)$, then what is its distribution function? Let's just use the definiti |
42,613 | Operations on probability distributions of continuous random variables | my 2 cents:
First, locally, if $\text{pdf}_s(s)$ is the probability density function of a random variable $s$, and let's do transform that $s = s(t)$, one would have $$\text{pdf}_t(t) = \text{pdf}_s(s(t)) \frac{ds(t)}{dt} ... (1)$$
Now let's look at $x \rightarrow \sin{x}$ transform.
Locally, one could replace (1) as ... | Operations on probability distributions of continuous random variables | my 2 cents:
First, locally, if $\text{pdf}_s(s)$ is the probability density function of a random variable $s$, and let's do transform that $s = s(t)$, one would have $$\text{pdf}_t(t) = \text{pdf}_s(s | Operations on probability distributions of continuous random variables
my 2 cents:
First, locally, if $\text{pdf}_s(s)$ is the probability density function of a random variable $s$, and let's do transform that $s = s(t)$, one would have $$\text{pdf}_t(t) = \text{pdf}_s(s(t)) \frac{ds(t)}{dt} ... (1)$$
Now let's look a... | Operations on probability distributions of continuous random variables
my 2 cents:
First, locally, if $\text{pdf}_s(s)$ is the probability density function of a random variable $s$, and let's do transform that $s = s(t)$, one would have $$\text{pdf}_t(t) = \text{pdf}_s(s |
42,614 | Binary classification when many binary features are missing | Assuming data are considered missing completely at random (cf. @whuber's comment), using an ensemble learning technique as described in the following paper might be interesting to try:
Polikar, R. et al. (2010).
Learn++.MF: A random subspace
approach for the missing feature
problem. Pattern Recognition,
43(11)... | Binary classification when many binary features are missing | Assuming data are considered missing completely at random (cf. @whuber's comment), using an ensemble learning technique as described in the following paper might be interesting to try:
Polikar, R. et | Binary classification when many binary features are missing
Assuming data are considered missing completely at random (cf. @whuber's comment), using an ensemble learning technique as described in the following paper might be interesting to try:
Polikar, R. et al. (2010).
Learn++.MF: A random subspace
approach for ... | Binary classification when many binary features are missing
Assuming data are considered missing completely at random (cf. @whuber's comment), using an ensemble learning technique as described in the following paper might be interesting to try:
Polikar, R. et |
42,615 | Binary classification when many binary features are missing | If the features in the subset are random you can still impute values. However, if you have that much missing data, I would think twice about whether or not you really have enough valid data to do any kind of analysis.
The multiple imputation FAQ page ---->
http://www.stat.psu.edu/~jls/mifaq.html | Binary classification when many binary features are missing | If the features in the subset are random you can still impute values. However, if you have that much missing data, I would think twice about whether or not you really have enough valid data to do any | Binary classification when many binary features are missing
If the features in the subset are random you can still impute values. However, if you have that much missing data, I would think twice about whether or not you really have enough valid data to do any kind of analysis.
The multiple imputation FAQ page ---->
htt... | Binary classification when many binary features are missing
If the features in the subset are random you can still impute values. However, if you have that much missing data, I would think twice about whether or not you really have enough valid data to do any |
42,616 | How to compute Confidence Interval associated to a Binomial proportion's increase? | Following whuber's link to Wikipedia you have
Assume that $a$ and $b$ are jointly
normally distributed, and that $b$ is
not too near zero (i.e. more
specifically, that the standard error
of $b$ is small compared to $b$)
$$\operatorname{Var} \left( \frac{a}{b} \right) = \left(
\frac{a}{b} \right)^{2} \left( ... | How to compute Confidence Interval associated to a Binomial proportion's increase? | Following whuber's link to Wikipedia you have
Assume that $a$ and $b$ are jointly
normally distributed, and that $b$ is
not too near zero (i.e. more
specifically, that the standard error
of | How to compute Confidence Interval associated to a Binomial proportion's increase?
Following whuber's link to Wikipedia you have
Assume that $a$ and $b$ are jointly
normally distributed, and that $b$ is
not too near zero (i.e. more
specifically, that the standard error
of $b$ is small compared to $b$)
$$\oper... | How to compute Confidence Interval associated to a Binomial proportion's increase?
Following whuber's link to Wikipedia you have
Assume that $a$ and $b$ are jointly
normally distributed, and that $b$ is
not too near zero (i.e. more
specifically, that the standard error
of |
42,617 | Predicting from a simple linear model with lags in R | It's clear the solution I posted previously is inadequate and inelegant. Here is my second attempt, which 100% solves my problem. Please let me know if you spot any bugs! I will cross post to stack overflow, if you all think that would be a better place to get comments on my code.
#A function to iteratively predict ... | Predicting from a simple linear model with lags in R | It's clear the solution I posted previously is inadequate and inelegant. Here is my second attempt, which 100% solves my problem. Please let me know if you spot any bugs! I will cross post to stack | Predicting from a simple linear model with lags in R
It's clear the solution I posted previously is inadequate and inelegant. Here is my second attempt, which 100% solves my problem. Please let me know if you spot any bugs! I will cross post to stack overflow, if you all think that would be a better place to get com... | Predicting from a simple linear model with lags in R
It's clear the solution I posted previously is inadequate and inelegant. Here is my second attempt, which 100% solves my problem. Please let me know if you spot any bugs! I will cross post to stack |
42,618 | Predicting from a simple linear model with lags in R | One more method, which has been suggested in other topics, is to just use the arima function with xregs. Arima seems to be able to make forecasts from a new set of xregs just fine. | Predicting from a simple linear model with lags in R | One more method, which has been suggested in other topics, is to just use the arima function with xregs. Arima seems to be able to make forecasts from a new set of xregs just fine. | Predicting from a simple linear model with lags in R
One more method, which has been suggested in other topics, is to just use the arima function with xregs. Arima seems to be able to make forecasts from a new set of xregs just fine. | Predicting from a simple linear model with lags in R
One more method, which has been suggested in other topics, is to just use the arima function with xregs. Arima seems to be able to make forecasts from a new set of xregs just fine. |
42,619 | Predicting from a simple linear model with lags in R | Ok, I answered my own problem, but my solution could use more testing and probably isn't perfect. Suggestions would be appreciated!
First of all I used a modified version of the parseCall function available here:
parseCall <- function(obj) {
if (class(obj) != 'call') {
stop("Must supply a 'call' object")
... | Predicting from a simple linear model with lags in R | Ok, I answered my own problem, but my solution could use more testing and probably isn't perfect. Suggestions would be appreciated!
First of all I used a modified version of the parseCall function av | Predicting from a simple linear model with lags in R
Ok, I answered my own problem, but my solution could use more testing and probably isn't perfect. Suggestions would be appreciated!
First of all I used a modified version of the parseCall function available here:
parseCall <- function(obj) {
if (class(obj) != 'c... | Predicting from a simple linear model with lags in R
Ok, I answered my own problem, but my solution could use more testing and probably isn't perfect. Suggestions would be appreciated!
First of all I used a modified version of the parseCall function av |
42,620 | Non-informative priors for the AR(1) model | This transformation group is discrete and finite: it contains exactly two elements, the identity and inverting $\rho$. It's simply not big enough to determine a prior. In fact, you can choose any measurable function $f$ defined on $[-1,1]$ provided (a) it is integrable and (b) $\rho^{-2}f(1/\rho)d\rho$ is integrable ... | Non-informative priors for the AR(1) model | This transformation group is discrete and finite: it contains exactly two elements, the identity and inverting $\rho$. It's simply not big enough to determine a prior. In fact, you can choose any me | Non-informative priors for the AR(1) model
This transformation group is discrete and finite: it contains exactly two elements, the identity and inverting $\rho$. It's simply not big enough to determine a prior. In fact, you can choose any measurable function $f$ defined on $[-1,1]$ provided (a) it is integrable and (... | Non-informative priors for the AR(1) model
This transformation group is discrete and finite: it contains exactly two elements, the identity and inverting $\rho$. It's simply not big enough to determine a prior. In fact, you can choose any me |
42,621 | When is the shrinkage applied in Friedman's stochastic gradient boosting machine? | Using trees, the shrinkage takes place at the update stage of the algorithm, when the new function $f(x)_k$ is created as the function prior step ($f(x)_{k-1}$) + the new decision tree output ($p(x)_k$). This new tree output ($p(x)_k$) is scaled by the learning rate parameter.
See for example the implementation in R GB... | When is the shrinkage applied in Friedman's stochastic gradient boosting machine? | Using trees, the shrinkage takes place at the update stage of the algorithm, when the new function $f(x)_k$ is created as the function prior step ($f(x)_{k-1}$) + the new decision tree output ($p(x)_k | When is the shrinkage applied in Friedman's stochastic gradient boosting machine?
Using trees, the shrinkage takes place at the update stage of the algorithm, when the new function $f(x)_k$ is created as the function prior step ($f(x)_{k-1}$) + the new decision tree output ($p(x)_k$). This new tree output ($p(x)_k$) is... | When is the shrinkage applied in Friedman's stochastic gradient boosting machine?
Using trees, the shrinkage takes place at the update stage of the algorithm, when the new function $f(x)_k$ is created as the function prior step ($f(x)_{k-1}$) + the new decision tree output ($p(x)_k |
42,622 | Use coefficients of thin plate regression splines in a clustering method | If I understand correctly, I think you want the coefficients from the $gam component:
> coef(test$gam)
(Intercept) s(x1).1 s(x1).2 s(x1).3 s(x1).4 s(x1).5
21.8323526 9.2169405 15.7504889 -3.4709907 16.9314057 -19.4909343
s(x1).6 s(x1).7 s(x1).8 s(x1).9 s(x2).1 s(x2).2... | Use coefficients of thin plate regression splines in a clustering method | If I understand correctly, I think you want the coefficients from the $gam component:
> coef(test$gam)
(Intercept) s(x1).1 s(x1).2 s(x1).3 s(x1).4 s(x1).5
21.8323526 9.2169405 | Use coefficients of thin plate regression splines in a clustering method
If I understand correctly, I think you want the coefficients from the $gam component:
> coef(test$gam)
(Intercept) s(x1).1 s(x1).2 s(x1).3 s(x1).4 s(x1).5
21.8323526 9.2169405 15.7504889 -3.4709907 16.9314057 -19.4909343... | Use coefficients of thin plate regression splines in a clustering method
If I understand correctly, I think you want the coefficients from the $gam component:
> coef(test$gam)
(Intercept) s(x1).1 s(x1).2 s(x1).3 s(x1).4 s(x1).5
21.8323526 9.2169405 |
42,623 | Measuring statistical significance of machine learning algorithms comparison | You have two biases to remove here -- the selection of the initial parameters set and the selection of train/test data.
So, I don't think it is good to compare algorithms based on the same initial parameters set; I would just run the evaluation over few different initial sets for each of the algorithms to get more gene... | Measuring statistical significance of machine learning algorithms comparison | You have two biases to remove here -- the selection of the initial parameters set and the selection of train/test data.
So, I don't think it is good to compare algorithms based on the same initial par | Measuring statistical significance of machine learning algorithms comparison
You have two biases to remove here -- the selection of the initial parameters set and the selection of train/test data.
So, I don't think it is good to compare algorithms based on the same initial parameters set; I would just run the evaluatio... | Measuring statistical significance of machine learning algorithms comparison
You have two biases to remove here -- the selection of the initial parameters set and the selection of train/test data.
So, I don't think it is good to compare algorithms based on the same initial par |
42,624 | Survival analysis, one cohort, two classifications | I'll concentrate on your example question: does class 1 of the old classification scheme have a better or worse survival than class 1 of the updated classification scheme?
We can form four mutually exclusive groups of patients:
(a) Patients who weren't in class 1 under either scheme. Clearly, they don't help us answer ... | Survival analysis, one cohort, two classifications | I'll concentrate on your example question: does class 1 of the old classification scheme have a better or worse survival than class 1 of the updated classification scheme?
We can form four mutually ex | Survival analysis, one cohort, two classifications
I'll concentrate on your example question: does class 1 of the old classification scheme have a better or worse survival than class 1 of the updated classification scheme?
We can form four mutually exclusive groups of patients:
(a) Patients who weren't in class 1 under... | Survival analysis, one cohort, two classifications
I'll concentrate on your example question: does class 1 of the old classification scheme have a better or worse survival than class 1 of the updated classification scheme?
We can form four mutually ex |
42,625 | How much can the "pyramid of evidence" be applied to economics and political sciences? | Way back in 1965, Sir Austin Bradford Hill wrote a great essay about something very akin to the Pyramid of Evidence, where he discussed how the piling up of evidence can increase our confidence in hypotheses of causality in Medicine.
Most of the factors he discusses can be applied to Economics and political sciences. | How much can the "pyramid of evidence" be applied to economics and political sciences? | Way back in 1965, Sir Austin Bradford Hill wrote a great essay about something very akin to the Pyramid of Evidence, where he discussed how the piling up of evidence can increase our confidence in hyp | How much can the "pyramid of evidence" be applied to economics and political sciences?
Way back in 1965, Sir Austin Bradford Hill wrote a great essay about something very akin to the Pyramid of Evidence, where he discussed how the piling up of evidence can increase our confidence in hypotheses of causality in Medicine.... | How much can the "pyramid of evidence" be applied to economics and political sciences?
Way back in 1965, Sir Austin Bradford Hill wrote a great essay about something very akin to the Pyramid of Evidence, where he discussed how the piling up of evidence can increase our confidence in hyp |
42,626 | Meaningful deviation measure with strongly varying datapoints | You need to use some paired test, maybe paired t-test or a sign test is the distribution is really weired. | Meaningful deviation measure with strongly varying datapoints | You need to use some paired test, maybe paired t-test or a sign test is the distribution is really weired. | Meaningful deviation measure with strongly varying datapoints
You need to use some paired test, maybe paired t-test or a sign test is the distribution is really weired. | Meaningful deviation measure with strongly varying datapoints
You need to use some paired test, maybe paired t-test or a sign test is the distribution is really weired. |
42,627 | Meaningful deviation measure with strongly varying datapoints | I am not at all sure if ignoring the performance spread is a good idea. Ideally, you would want a method to be both reliable (i.e., have low spread) and be valid (i.e., give a performance measure of close to 1). Consider the following two output measures:
Method 1. [0.80, 0.60]
Method 2. [0.71, 0.69].
Unlike your exa... | Meaningful deviation measure with strongly varying datapoints | I am not at all sure if ignoring the performance spread is a good idea. Ideally, you would want a method to be both reliable (i.e., have low spread) and be valid (i.e., give a performance measure of c | Meaningful deviation measure with strongly varying datapoints
I am not at all sure if ignoring the performance spread is a good idea. Ideally, you would want a method to be both reliable (i.e., have low spread) and be valid (i.e., give a performance measure of close to 1). Consider the following two output measures:
M... | Meaningful deviation measure with strongly varying datapoints
I am not at all sure if ignoring the performance spread is a good idea. Ideally, you would want a method to be both reliable (i.e., have low spread) and be valid (i.e., give a performance measure of c |
42,628 | Sample problems on logit modeling and Bayesian methods | The UCLA Statistical Computing site has a number of examples in various languages (SAS, R, etc). In particular, see the following pages (look among the links titled logistic regression, categorical data analysis and generalized linear models):
Data Analysis Examples
Textbook Examples | Sample problems on logit modeling and Bayesian methods | The UCLA Statistical Computing site has a number of examples in various languages (SAS, R, etc). In particular, see the following pages (look among the links titled logistic regression, categorical d | Sample problems on logit modeling and Bayesian methods
The UCLA Statistical Computing site has a number of examples in various languages (SAS, R, etc). In particular, see the following pages (look among the links titled logistic regression, categorical data analysis and generalized linear models):
Data Analysis Examp... | Sample problems on logit modeling and Bayesian methods
The UCLA Statistical Computing site has a number of examples in various languages (SAS, R, etc). In particular, see the following pages (look among the links titled logistic regression, categorical d |
42,629 | Optimizing the sample size: number of individuals versus trials per individual | The mean per individual will be distributed as
$$\bar{Y}_i = \frac{1}{n_i} \sum_{j = 1}^{n_i} Y_{ij} \sim N\left(\mu, \sigma^2 + \tau^2/n_i\right)$$
where $n_i \geq 1$ are the number of observations for individual $i$ (we need at least 1 measurement for a participant).
The estimate will be
$$\hat{\mu} = \sum_{i=1}^n w_... | Optimizing the sample size: number of individuals versus trials per individual | The mean per individual will be distributed as
$$\bar{Y}_i = \frac{1}{n_i} \sum_{j = 1}^{n_i} Y_{ij} \sim N\left(\mu, \sigma^2 + \tau^2/n_i\right)$$
where $n_i \geq 1$ are the number of observations f | Optimizing the sample size: number of individuals versus trials per individual
The mean per individual will be distributed as
$$\bar{Y}_i = \frac{1}{n_i} \sum_{j = 1}^{n_i} Y_{ij} \sim N\left(\mu, \sigma^2 + \tau^2/n_i\right)$$
where $n_i \geq 1$ are the number of observations for individual $i$ (we need at least 1 mea... | Optimizing the sample size: number of individuals versus trials per individual
The mean per individual will be distributed as
$$\bar{Y}_i = \frac{1}{n_i} \sum_{j = 1}^{n_i} Y_{ij} \sim N\left(\mu, \sigma^2 + \tau^2/n_i\right)$$
where $n_i \geq 1$ are the number of observations f |
42,630 | Find UMVUE of difference of parameters of two exponential distribution random variables | Changing the question in two different ways allows for some answers:
If $\theta_x$ and $\theta_y$ are rate rather than scale parameters,
$$
\frac{n-1}{n} \frac{\sum_{i=1}^n (1-\Delta_i)}{\sum_{i=1} Z_i} - \frac{n-1}{n} \frac{\sum_{i=1}^n \Delta_i}{\sum_{i=1} Z_i}\tag{1}
$$
is an unbiased estimator of $1/\theta_x - 1/\... | Find UMVUE of difference of parameters of two exponential distribution random variables | Changing the question in two different ways allows for some answers:
If $\theta_x$ and $\theta_y$ are rate rather than scale parameters,
$$
\frac{n-1}{n} \frac{\sum_{i=1}^n (1-\Delta_i)}{\sum_{i=1} Z | Find UMVUE of difference of parameters of two exponential distribution random variables
Changing the question in two different ways allows for some answers:
If $\theta_x$ and $\theta_y$ are rate rather than scale parameters,
$$
\frac{n-1}{n} \frac{\sum_{i=1}^n (1-\Delta_i)}{\sum_{i=1} Z_i} - \frac{n-1}{n} \frac{\sum_{... | Find UMVUE of difference of parameters of two exponential distribution random variables
Changing the question in two different ways allows for some answers:
If $\theta_x$ and $\theta_y$ are rate rather than scale parameters,
$$
\frac{n-1}{n} \frac{\sum_{i=1}^n (1-\Delta_i)}{\sum_{i=1} Z |
42,631 | How can you combine control variates with antithetic variates | Using antithetic variates to improve the Monte Carlo approximation of $\mathbb E^F[h(X)]$ mean generating correlated realisations from $F$, $X_1,\ldots,X_n$ such that$$\text{var}(h(X_1)+\cdots+h(X_n))<\text{var}(h(X_1))+\cdots+\text{var}(h(X_n))\tag{1}$$
While the idea is appealing, it is difficult to implement in real... | How can you combine control variates with antithetic variates | Using antithetic variates to improve the Monte Carlo approximation of $\mathbb E^F[h(X)]$ mean generating correlated realisations from $F$, $X_1,\ldots,X_n$ such that$$\text{var}(h(X_1)+\cdots+h(X_n)) | How can you combine control variates with antithetic variates
Using antithetic variates to improve the Monte Carlo approximation of $\mathbb E^F[h(X)]$ mean generating correlated realisations from $F$, $X_1,\ldots,X_n$ such that$$\text{var}(h(X_1)+\cdots+h(X_n))<\text{var}(h(X_1))+\cdots+\text{var}(h(X_n))\tag{1}$$
Whi... | How can you combine control variates with antithetic variates
Using antithetic variates to improve the Monte Carlo approximation of $\mathbb E^F[h(X)]$ mean generating correlated realisations from $F$, $X_1,\ldots,X_n$ such that$$\text{var}(h(X_1)+\cdots+h(X_n)) |
42,632 | Bootstrap for random effects logistic regression to get CI for difference in proportions | More of an extended set of comments than an answer.
The bootMer() and its associated simulate.merMod() functions in lme4 contain hints as to what works in practice whether in frequentist or Bayesian modeling. (The package doesn't seem to contain functions for case resampling; more on that later.) Quotes below are from ... | Bootstrap for random effects logistic regression to get CI for difference in proportions | More of an extended set of comments than an answer.
The bootMer() and its associated simulate.merMod() functions in lme4 contain hints as to what works in practice whether in frequentist or Bayesian m | Bootstrap for random effects logistic regression to get CI for difference in proportions
More of an extended set of comments than an answer.
The bootMer() and its associated simulate.merMod() functions in lme4 contain hints as to what works in practice whether in frequentist or Bayesian modeling. (The package doesn't s... | Bootstrap for random effects logistic regression to get CI for difference in proportions
More of an extended set of comments than an answer.
The bootMer() and its associated simulate.merMod() functions in lme4 contain hints as to what works in practice whether in frequentist or Bayesian m |
42,633 | Is there something like a confusion matrix for a probabilistic score rather than categories? | This is only a partial answer as this comes from my personal experience of training classifiers rather than the literature.
Many classifiers output a weight (or probability) for each class simultaneously, which means the weights are paired by the example from the data set. The approach I have taken is to treat this res... | Is there something like a confusion matrix for a probabilistic score rather than categories? | This is only a partial answer as this comes from my personal experience of training classifiers rather than the literature.
Many classifiers output a weight (or probability) for each class simultaneou | Is there something like a confusion matrix for a probabilistic score rather than categories?
This is only a partial answer as this comes from my personal experience of training classifiers rather than the literature.
Many classifiers output a weight (or probability) for each class simultaneously, which means the weight... | Is there something like a confusion matrix for a probabilistic score rather than categories?
This is only a partial answer as this comes from my personal experience of training classifiers rather than the literature.
Many classifiers output a weight (or probability) for each class simultaneou |
42,634 | Is there something like a confusion matrix for a probabilistic score rather than categories? | I have never come across such a thing in literature, but it is a very interesting idea. Firstly, I'd like to point out that normalised confusion matrices exist (I know this isn't what you are asking for but it will illustrate a point I'm going to make, so just bare with me); for these types of confusion matrix there is... | Is there something like a confusion matrix for a probabilistic score rather than categories? | I have never come across such a thing in literature, but it is a very interesting idea. Firstly, I'd like to point out that normalised confusion matrices exist (I know this isn't what you are asking f | Is there something like a confusion matrix for a probabilistic score rather than categories?
I have never come across such a thing in literature, but it is a very interesting idea. Firstly, I'd like to point out that normalised confusion matrices exist (I know this isn't what you are asking for but it will illustrate a... | Is there something like a confusion matrix for a probabilistic score rather than categories?
I have never come across such a thing in literature, but it is a very interesting idea. Firstly, I'd like to point out that normalised confusion matrices exist (I know this isn't what you are asking f |
42,635 | Understanding the Benjamini-Hochberg method proof | Let's backup and give more context to the two cases specified by equation (5), and then clarify the reasoning behind some of the details.
In equation (5), we consider the two cases, $p \leq p''$ and $ p > p''$
1. $P'_{m_0} =p\leq p''$
This is a difficult (rare) case because all the $m_0$ p-values associated with true n... | Understanding the Benjamini-Hochberg method proof | Let's backup and give more context to the two cases specified by equation (5), and then clarify the reasoning behind some of the details.
In equation (5), we consider the two cases, $p \leq p''$ and $ | Understanding the Benjamini-Hochberg method proof
Let's backup and give more context to the two cases specified by equation (5), and then clarify the reasoning behind some of the details.
In equation (5), we consider the two cases, $p \leq p''$ and $ p > p''$
1. $P'_{m_0} =p\leq p''$
This is a difficult (rare) case bec... | Understanding the Benjamini-Hochberg method proof
Let's backup and give more context to the two cases specified by equation (5), and then clarify the reasoning behind some of the details.
In equation (5), we consider the two cases, $p \leq p''$ and $ |
42,636 | What exactly is the gblinear booster in XGBoost? | It is just using a linear model with l1 and l2 regularization as its base learner rather than a decision tree. Here is a similar Q&A: Difference in regression coefficients of sklearn's LinearRegression and XGBRegressor .
So it will be different than other linear models because it is optimized slightly differently but m... | What exactly is the gblinear booster in XGBoost? | It is just using a linear model with l1 and l2 regularization as its base learner rather than a decision tree. Here is a similar Q&A: Difference in regression coefficients of sklearn's LinearRegressio | What exactly is the gblinear booster in XGBoost?
It is just using a linear model with l1 and l2 regularization as its base learner rather than a decision tree. Here is a similar Q&A: Difference in regression coefficients of sklearn's LinearRegression and XGBRegressor .
So it will be different than other linear models b... | What exactly is the gblinear booster in XGBoost?
It is just using a linear model with l1 and l2 regularization as its base learner rather than a decision tree. Here is a similar Q&A: Difference in regression coefficients of sklearn's LinearRegressio |
42,637 | GAM: Do shrinkage smooth splines also address for concurvity? | In short, no, using select = TRUE doesn't automatically drop concurved terms. You should still check the concurvity of the terms in the resultant model, and decide whether to drop terms or not for highly concurved ones, checking how the other estimated terms change when you drop a concurved term.
That said, fitting wit... | GAM: Do shrinkage smooth splines also address for concurvity? | In short, no, using select = TRUE doesn't automatically drop concurved terms. You should still check the concurvity of the terms in the resultant model, and decide whether to drop terms or not for hig | GAM: Do shrinkage smooth splines also address for concurvity?
In short, no, using select = TRUE doesn't automatically drop concurved terms. You should still check the concurvity of the terms in the resultant model, and decide whether to drop terms or not for highly concurved ones, checking how the other estimated terms... | GAM: Do shrinkage smooth splines also address for concurvity?
In short, no, using select = TRUE doesn't automatically drop concurved terms. You should still check the concurvity of the terms in the resultant model, and decide whether to drop terms or not for hig |
42,638 | How to set up a DL classification model so that it selects from an ever changing menu | For matching problems, there are mainly two approaches:
Single network to embed object A and object B: In natural language processing, the input would be "[CLS] SentenceA [SEP] SentenceB [SEP]". Then the neural network would measure the difference between the two sentences. In computer vision, you would need to concat... | How to set up a DL classification model so that it selects from an ever changing menu | For matching problems, there are mainly two approaches:
Single network to embed object A and object B: In natural language processing, the input would be "[CLS] SentenceA [SEP] SentenceB [SEP]". Then | How to set up a DL classification model so that it selects from an ever changing menu
For matching problems, there are mainly two approaches:
Single network to embed object A and object B: In natural language processing, the input would be "[CLS] SentenceA [SEP] SentenceB [SEP]". Then the neural network would measure ... | How to set up a DL classification model so that it selects from an ever changing menu
For matching problems, there are mainly two approaches:
Single network to embed object A and object B: In natural language processing, the input would be "[CLS] SentenceA [SEP] SentenceB [SEP]". Then |
42,639 | How to set up a DL classification model so that it selects from an ever changing menu | NOTE: I'm still editing this post, need a bit more time to finish
Here's a philosophical aside on the limits of few-shot learning. It is not exactly the answer to the question, but I guess it could help set the expectations straight.
Part 1: Naive estimate of number of samples needed for classifier convergence. Let's s... | How to set up a DL classification model so that it selects from an ever changing menu | NOTE: I'm still editing this post, need a bit more time to finish
Here's a philosophical aside on the limits of few-shot learning. It is not exactly the answer to the question, but I guess it could he | How to set up a DL classification model so that it selects from an ever changing menu
NOTE: I'm still editing this post, need a bit more time to finish
Here's a philosophical aside on the limits of few-shot learning. It is not exactly the answer to the question, but I guess it could help set the expectations straight.
... | How to set up a DL classification model so that it selects from an ever changing menu
NOTE: I'm still editing this post, need a bit more time to finish
Here's a philosophical aside on the limits of few-shot learning. It is not exactly the answer to the question, but I guess it could he |
42,640 | 3 Treatment Agronomic Experiment: Latin Square or Randomized Complete Block Design with 4 replicates? | It is more natural to compare designs with equal number of observations, so I will compare a $3\times 3$ latin square (LSQ) with a thrice replicated RCBD. The LSQ leaves 2 df (defgrees of freedom) for error, while the RCBD leaves 4 df for error. So the advantage of the RCBD is more df for error, while the LSQ possibly ... | 3 Treatment Agronomic Experiment: Latin Square or Randomized Complete Block Design with 4 replicates | It is more natural to compare designs with equal number of observations, so I will compare a $3\times 3$ latin square (LSQ) with a thrice replicated RCBD. The LSQ leaves 2 df (defgrees of freedom) for | 3 Treatment Agronomic Experiment: Latin Square or Randomized Complete Block Design with 4 replicates?
It is more natural to compare designs with equal number of observations, so I will compare a $3\times 3$ latin square (LSQ) with a thrice replicated RCBD. The LSQ leaves 2 df (defgrees of freedom) for error, while the ... | 3 Treatment Agronomic Experiment: Latin Square or Randomized Complete Block Design with 4 replicates
It is more natural to compare designs with equal number of observations, so I will compare a $3\times 3$ latin square (LSQ) with a thrice replicated RCBD. The LSQ leaves 2 df (defgrees of freedom) for |
42,641 | What do you do after your tuned model perform badly on the test set? | Generally speaking, this should not be the case, and is most likely an implementation bug. The validation performance should be very close to the test performance. If this is not the case, either:
A) [Most likely] the code has one of the following mistakes:
Possibility 1: Incorrect preprocessing of the test set. E.g. ... | What do you do after your tuned model perform badly on the test set? | Generally speaking, this should not be the case, and is most likely an implementation bug. The validation performance should be very close to the test performance. If this is not the case, either:
A) | What do you do after your tuned model perform badly on the test set?
Generally speaking, this should not be the case, and is most likely an implementation bug. The validation performance should be very close to the test performance. If this is not the case, either:
A) [Most likely] the code has one of the following mis... | What do you do after your tuned model perform badly on the test set?
Generally speaking, this should not be the case, and is most likely an implementation bug. The validation performance should be very close to the test performance. If this is not the case, either:
A) |
42,642 | What do you do after your tuned model perform badly on the test set? | You have overfitted the training set. Try again with more data, or with some form of regularization, possibly including added noise. | What do you do after your tuned model perform badly on the test set? | You have overfitted the training set. Try again with more data, or with some form of regularization, possibly including added noise. | What do you do after your tuned model perform badly on the test set?
You have overfitted the training set. Try again with more data, or with some form of regularization, possibly including added noise. | What do you do after your tuned model perform badly on the test set?
You have overfitted the training set. Try again with more data, or with some form of regularization, possibly including added noise. |
42,643 | What do you do after your tuned model perform badly on the test set? | This may be due to your dev set and test set not being identically distributed.
One way to test this is to train a classifier that discriminates between the training/dev vs the test set.
If your dataset is small you should definitely check if the drop from dev/test metrics is consistent between splits. If the drop vari... | What do you do after your tuned model perform badly on the test set? | This may be due to your dev set and test set not being identically distributed.
One way to test this is to train a classifier that discriminates between the training/dev vs the test set.
If your datas | What do you do after your tuned model perform badly on the test set?
This may be due to your dev set and test set not being identically distributed.
One way to test this is to train a classifier that discriminates between the training/dev vs the test set.
If your dataset is small you should definitely check if the drop... | What do you do after your tuned model perform badly on the test set?
This may be due to your dev set and test set not being identically distributed.
One way to test this is to train a classifier that discriminates between the training/dev vs the test set.
If your datas |
42,644 | DAG: no back-door paths but background information shows a need for adjusting | What is the difference between disjunctive cause criterion and Pearl's single door criterion?
The Single Door Criterion establishes conditions under which a causal bath between two variables, say $X \rightarrow Y$, will be consistently estimated by the regression coefficient for $X$ in a multivariable regression model... | DAG: no back-door paths but background information shows a need for adjusting | What is the difference between disjunctive cause criterion and Pearl's single door criterion?
The Single Door Criterion establishes conditions under which a causal bath between two variables, say $X | DAG: no back-door paths but background information shows a need for adjusting
What is the difference between disjunctive cause criterion and Pearl's single door criterion?
The Single Door Criterion establishes conditions under which a causal bath between two variables, say $X \rightarrow Y$, will be consistently estim... | DAG: no back-door paths but background information shows a need for adjusting
What is the difference between disjunctive cause criterion and Pearl's single door criterion?
The Single Door Criterion establishes conditions under which a causal bath between two variables, say $X |
42,645 | How to make sure that the random sample is representative for the whole sample? | So long as you have no wish to incorporate covariate information into your sampling scheme (e.g., balancing tweets from males/females), the usual method is to take a simple random sample without replacement. This can be implemented in R using the sample.int function. In the code below I show you how to generate a sim... | How to make sure that the random sample is representative for the whole sample? | So long as you have no wish to incorporate covariate information into your sampling scheme (e.g., balancing tweets from males/females), the usual method is to take a simple random sample without repla | How to make sure that the random sample is representative for the whole sample?
So long as you have no wish to incorporate covariate information into your sampling scheme (e.g., balancing tweets from males/females), the usual method is to take a simple random sample without replacement. This can be implemented in R us... | How to make sure that the random sample is representative for the whole sample?
So long as you have no wish to incorporate covariate information into your sampling scheme (e.g., balancing tweets from males/females), the usual method is to take a simple random sample without repla |
42,646 | How to make sure that the random sample is representative for the whole sample? | Here I prefer the technique of Systematic Sampling where one selects every kth individual from the population. Thus, from a list of n arrived tweets, every kth tweet is chosen to construct a sample set of 's' tweets, such that k*s is close to n.
Advantages:
Simple statistically valid procedure
Accurate
Easier to imp... | How to make sure that the random sample is representative for the whole sample? | Here I prefer the technique of Systematic Sampling where one selects every kth individual from the population. Thus, from a list of n arrived tweets, every kth tweet is chosen to construct a sample se | How to make sure that the random sample is representative for the whole sample?
Here I prefer the technique of Systematic Sampling where one selects every kth individual from the population. Thus, from a list of n arrived tweets, every kth tweet is chosen to construct a sample set of 's' tweets, such that k*s is close ... | How to make sure that the random sample is representative for the whole sample?
Here I prefer the technique of Systematic Sampling where one selects every kth individual from the population. Thus, from a list of n arrived tweets, every kth tweet is chosen to construct a sample se |
42,647 | How to make sure that the random sample is representative for the whole sample? | What you want is a sample that is representative in terms of the topics you are going to manually code.
First of all, you want to be sure that your coding procedure is not biased. This is really important because a representative sample is useless if your coding procedure is biased. Thus you need at least two independe... | How to make sure that the random sample is representative for the whole sample? | What you want is a sample that is representative in terms of the topics you are going to manually code.
First of all, you want to be sure that your coding procedure is not biased. This is really impor | How to make sure that the random sample is representative for the whole sample?
What you want is a sample that is representative in terms of the topics you are going to manually code.
First of all, you want to be sure that your coding procedure is not biased. This is really important because a representative sample is ... | How to make sure that the random sample is representative for the whole sample?
What you want is a sample that is representative in terms of the topics you are going to manually code.
First of all, you want to be sure that your coding procedure is not biased. This is really impor |
42,648 | Interpretation of mutual information | The mutual information measure $I(X;Y)$ is nonparametric measure of probabilistic dependence between the variables $X$ and $Y$. As follows from wikipedia:
"Intuitively, mutual information measures the information that $X$ and $Y$ share: It measures how much knowing one of these variables reduces uncertainty about the o... | Interpretation of mutual information | The mutual information measure $I(X;Y)$ is nonparametric measure of probabilistic dependence between the variables $X$ and $Y$. As follows from wikipedia:
"Intuitively, mutual information measures the | Interpretation of mutual information
The mutual information measure $I(X;Y)$ is nonparametric measure of probabilistic dependence between the variables $X$ and $Y$. As follows from wikipedia:
"Intuitively, mutual information measures the information that $X$ and $Y$ share: It measures how much knowing one of these vari... | Interpretation of mutual information
The mutual information measure $I(X;Y)$ is nonparametric measure of probabilistic dependence between the variables $X$ and $Y$. As follows from wikipedia:
"Intuitively, mutual information measures the |
42,649 | Interpretation of mutual information | From a Kaggle webpage:
The least possible mutual information between quantities is 0.0. When
MI is zero, the quantities are independent: neither can tell you
anything about the other. Conversely, in theory there's no upper bound
to what MI can be. In practice though values above 2.0 or so are
uncommon. (Mutual informa... | Interpretation of mutual information | From a Kaggle webpage:
The least possible mutual information between quantities is 0.0. When
MI is zero, the quantities are independent: neither can tell you
anything about the other. Conversely, in | Interpretation of mutual information
From a Kaggle webpage:
The least possible mutual information between quantities is 0.0. When
MI is zero, the quantities are independent: neither can tell you
anything about the other. Conversely, in theory there's no upper bound
to what MI can be. In practice though values above 2.... | Interpretation of mutual information
From a Kaggle webpage:
The least possible mutual information between quantities is 0.0. When
MI is zero, the quantities are independent: neither can tell you
anything about the other. Conversely, in |
42,650 | How is pairwise PERMANOVA/adonis a valid non-parametric approach for pairwise comparisons | Am I incorrect in saying that this is equivalent to a simple series of pairwise anovas with p-values calculated according to the observed F statistics probability under the empirical null distribution that was generated through random permutations of group membership (or "location" membership in this case).
No, you ar... | How is pairwise PERMANOVA/adonis a valid non-parametric approach for pairwise comparisons | Am I incorrect in saying that this is equivalent to a simple series of pairwise anovas with p-values calculated according to the observed F statistics probability under the empirical null distribution | How is pairwise PERMANOVA/adonis a valid non-parametric approach for pairwise comparisons
Am I incorrect in saying that this is equivalent to a simple series of pairwise anovas with p-values calculated according to the observed F statistics probability under the empirical null distribution that was generated through ra... | How is pairwise PERMANOVA/adonis a valid non-parametric approach for pairwise comparisons
Am I incorrect in saying that this is equivalent to a simple series of pairwise anovas with p-values calculated according to the observed F statistics probability under the empirical null distribution |
42,651 | Intuition for why the (log) partition function matters? | This is how Self-Normalized Importance Sampling (SNIS) works - you draw samples from a proposal distribution that is essentially guess about where
This shows how the lack of knowledge about $\log Z$ can be solved.
But it doesn't mean that lack of knowledge of $\log Z$ is not a problem.
In fact the SNIS method shows th... | Intuition for why the (log) partition function matters? | This is how Self-Normalized Importance Sampling (SNIS) works - you draw samples from a proposal distribution that is essentially guess about where
This shows how the lack of knowledge about $\log Z$ | Intuition for why the (log) partition function matters?
This is how Self-Normalized Importance Sampling (SNIS) works - you draw samples from a proposal distribution that is essentially guess about where
This shows how the lack of knowledge about $\log Z$ can be solved.
But it doesn't mean that lack of knowledge of $\l... | Intuition for why the (log) partition function matters?
This is how Self-Normalized Importance Sampling (SNIS) works - you draw samples from a proposal distribution that is essentially guess about where
This shows how the lack of knowledge about $\log Z$ |
42,652 | Intuition for why the (log) partition function matters? | As a precursor: It is worth thinking about how these problems arise in statistical practice. Optimising over $x$ is rare - usually, $x$ has already been observed. It is more common to be optimising over $\boldsymbol{\theta}$, given an observation $x$, e.g. to find the maximum likelihood estimator of $\theta$, one would... | Intuition for why the (log) partition function matters? | As a precursor: It is worth thinking about how these problems arise in statistical practice. Optimising over $x$ is rare - usually, $x$ has already been observed. It is more common to be optimising ov | Intuition for why the (log) partition function matters?
As a precursor: It is worth thinking about how these problems arise in statistical practice. Optimising over $x$ is rare - usually, $x$ has already been observed. It is more common to be optimising over $\boldsymbol{\theta}$, given an observation $x$, e.g. to find... | Intuition for why the (log) partition function matters?
As a precursor: It is worth thinking about how these problems arise in statistical practice. Optimising over $x$ is rare - usually, $x$ has already been observed. It is more common to be optimising ov |
42,653 | Combining Bootstrap and Cross-Validation | The bootstrap is certainly one way of assessing internal validation of a model. Ewout W. Steyerberg in his book Clinical Prediction Models describes how the bootstrap can be used to estimate optimism corrected performance. The procedure is as follows:
Construct a model in the original sample; determine the apparent ... | Combining Bootstrap and Cross-Validation | The bootstrap is certainly one way of assessing internal validation of a model. Ewout W. Steyerberg in his book Clinical Prediction Models describes how the bootstrap can be used to estimate optimism | Combining Bootstrap and Cross-Validation
The bootstrap is certainly one way of assessing internal validation of a model. Ewout W. Steyerberg in his book Clinical Prediction Models describes how the bootstrap can be used to estimate optimism corrected performance. The procedure is as follows:
Construct a model in the... | Combining Bootstrap and Cross-Validation
The bootstrap is certainly one way of assessing internal validation of a model. Ewout W. Steyerberg in his book Clinical Prediction Models describes how the bootstrap can be used to estimate optimism |
42,654 | Intuition behind Partial Residual Plots | While it is mentioned in a number of regression texts, the plot you have mentioned here does not seem particularly useful to me. A far better alternative is the added variable plot, which correctly represents the relationship between an individual explanatory variable and the response variable conditional on other exp... | Intuition behind Partial Residual Plots | While it is mentioned in a number of regression texts, the plot you have mentioned here does not seem particularly useful to me. A far better alternative is the added variable plot, which correctly r | Intuition behind Partial Residual Plots
While it is mentioned in a number of regression texts, the plot you have mentioned here does not seem particularly useful to me. A far better alternative is the added variable plot, which correctly represents the relationship between an individual explanatory variable and the re... | Intuition behind Partial Residual Plots
While it is mentioned in a number of regression texts, the plot you have mentioned here does not seem particularly useful to me. A far better alternative is the added variable plot, which correctly r |
42,655 | The "correct" way to approximate $\text{var}(f(X))$ via Taylor expansion | I cannot speak to the derivation of the first approximation (which looks wrong to me). However, the second equation is obtained using a second-order Taylor approximation to $f$ for the case where the underlying distribution is centred, unskewed and mesokurtic. In this case, you have $\mu=0$, $\gamma=0$ and $\kappa=3$... | The "correct" way to approximate $\text{var}(f(X))$ via Taylor expansion | I cannot speak to the derivation of the first approximation (which looks wrong to me). However, the second equation is obtained using a second-order Taylor approximation to $f$ for the case where the | The "correct" way to approximate $\text{var}(f(X))$ via Taylor expansion
I cannot speak to the derivation of the first approximation (which looks wrong to me). However, the second equation is obtained using a second-order Taylor approximation to $f$ for the case where the underlying distribution is centred, unskewed a... | The "correct" way to approximate $\text{var}(f(X))$ via Taylor expansion
I cannot speak to the derivation of the first approximation (which looks wrong to me). However, the second equation is obtained using a second-order Taylor approximation to $f$ for the case where the |
42,656 | Interpretation of zero-truncated Poisson regression coefficients | Unfortunately not. There is (to the best of my knowledge) no easy ceteris paribus interpretation of the coefficients' effect on the expectation of the zero-truncated response.
Sometimes the usual multiplicative effect on $\lambda$ has a natural interpretation. Namely, if the expectation of the underlying untruncated co... | Interpretation of zero-truncated Poisson regression coefficients | Unfortunately not. There is (to the best of my knowledge) no easy ceteris paribus interpretation of the coefficients' effect on the expectation of the zero-truncated response.
Sometimes the usual mult | Interpretation of zero-truncated Poisson regression coefficients
Unfortunately not. There is (to the best of my knowledge) no easy ceteris paribus interpretation of the coefficients' effect on the expectation of the zero-truncated response.
Sometimes the usual multiplicative effect on $\lambda$ has a natural interpreta... | Interpretation of zero-truncated Poisson regression coefficients
Unfortunately not. There is (to the best of my knowledge) no easy ceteris paribus interpretation of the coefficients' effect on the expectation of the zero-truncated response.
Sometimes the usual mult |
42,657 | Forecasting/predicting total sum of donations (following GLM with poisson family and log link) | ROUND TWO:
You asked “how do I do this with the log-link function and quasi(Poisson) errors?”. I say put aside your priors suggesting a particular fixed model and use a data-driven empirical process to identify the (possible) memory model, refining parameters and testing both necessity and sufficiency.
When you only h... | Forecasting/predicting total sum of donations (following GLM with poisson family and log link) | ROUND TWO:
You asked “how do I do this with the log-link function and quasi(Poisson) errors?”. I say put aside your priors suggesting a particular fixed model and use a data-driven empirical process t | Forecasting/predicting total sum of donations (following GLM with poisson family and log link)
ROUND TWO:
You asked “how do I do this with the log-link function and quasi(Poisson) errors?”. I say put aside your priors suggesting a particular fixed model and use a data-driven empirical process to identify the (possible)... | Forecasting/predicting total sum of donations (following GLM with poisson family and log link)
ROUND TWO:
You asked “how do I do this with the log-link function and quasi(Poisson) errors?”. I say put aside your priors suggesting a particular fixed model and use a data-driven empirical process t |
42,658 | Forecasting/predicting total sum of donations (following GLM with poisson family and log link) | I took your 29 days (oldest to newest) and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here
All models are wrong ... but some are useful .... . It is fundamentally an autoregressive process of order 1 after one has adjusted for the three "unusual data points " see f... | Forecasting/predicting total sum of donations (following GLM with poisson family and log link) | I took your 29 days (oldest to newest) and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here
All models are wrong ... but some are useful .... . It i | Forecasting/predicting total sum of donations (following GLM with poisson family and log link)
I took your 29 days (oldest to newest) and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here
All models are wrong ... but some are useful .... . It is fundamentally an autore... | Forecasting/predicting total sum of donations (following GLM with poisson family and log link)
I took your 29 days (oldest to newest) and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here
All models are wrong ... but some are useful .... . It i |
42,659 | Forecasting/predicting total sum of donations (following GLM with poisson family and log link) | For this type of problem, it should be possible to make a prediction of the total donations by predicting the infinite tail of donations, and adding this to the observed donations. To facilitate our analysis, suppose we let $M_t$ denote the donation received on day $t$, and let $U$ denote the total remaining donations... | Forecasting/predicting total sum of donations (following GLM with poisson family and log link) | For this type of problem, it should be possible to make a prediction of the total donations by predicting the infinite tail of donations, and adding this to the observed donations. To facilitate our | Forecasting/predicting total sum of donations (following GLM with poisson family and log link)
For this type of problem, it should be possible to make a prediction of the total donations by predicting the infinite tail of donations, and adding this to the observed donations. To facilitate our analysis, suppose we let ... | Forecasting/predicting total sum of donations (following GLM with poisson family and log link)
For this type of problem, it should be possible to make a prediction of the total donations by predicting the infinite tail of donations, and adding this to the observed donations. To facilitate our |
42,660 | Bootstrapped confidence intervals for performance metrics of predictive models | the goal is to build a predictive model.
I read this as: a model that is then actually used for prediction, and we need to know the performance of exactly that model*
Independent Test Set or Hold Out Testing
Now, if you set up is that you have a training set that is used to build your model, and once that model is fin... | Bootstrapped confidence intervals for performance metrics of predictive models | the goal is to build a predictive model.
I read this as: a model that is then actually used for prediction, and we need to know the performance of exactly that model*
Independent Test Set or Hold Out | Bootstrapped confidence intervals for performance metrics of predictive models
the goal is to build a predictive model.
I read this as: a model that is then actually used for prediction, and we need to know the performance of exactly that model*
Independent Test Set or Hold Out Testing
Now, if you set up is that you h... | Bootstrapped confidence intervals for performance metrics of predictive models
the goal is to build a predictive model.
I read this as: a model that is then actually used for prediction, and we need to know the performance of exactly that model*
Independent Test Set or Hold Out |
42,661 | Bootstrapped confidence intervals for performance metrics of predictive models | If you are just evaluating predictions of a model on one test set, so no CV than this is a simple problem and you can just treat your model predictions as you would any other variable in order to get some estimates and their CI.
So if you want to get CI using bootstrap, then you don't need to refit the model many time... | Bootstrapped confidence intervals for performance metrics of predictive models | If you are just evaluating predictions of a model on one test set, so no CV than this is a simple problem and you can just treat your model predictions as you would any other variable in order to get | Bootstrapped confidence intervals for performance metrics of predictive models
If you are just evaluating predictions of a model on one test set, so no CV than this is a simple problem and you can just treat your model predictions as you would any other variable in order to get some estimates and their CI.
So if you w... | Bootstrapped confidence intervals for performance metrics of predictive models
If you are just evaluating predictions of a model on one test set, so no CV than this is a simple problem and you can just treat your model predictions as you would any other variable in order to get |
42,662 | Simulate a Continuous Joint pdf in R Using Known Distributions | There are many ways you can simulate this bivariate random vector. Probably the most efficient way is to derive the marginal distribution of one variable, and the conditional distribution of the other, and then simulate the variables individually using these distributions.
An alternative method, which is less efficien... | Simulate a Continuous Joint pdf in R Using Known Distributions | There are many ways you can simulate this bivariate random vector. Probably the most efficient way is to derive the marginal distribution of one variable, and the conditional distribution of the othe | Simulate a Continuous Joint pdf in R Using Known Distributions
There are many ways you can simulate this bivariate random vector. Probably the most efficient way is to derive the marginal distribution of one variable, and the conditional distribution of the other, and then simulate the variables individually using the... | Simulate a Continuous Joint pdf in R Using Known Distributions
There are many ways you can simulate this bivariate random vector. Probably the most efficient way is to derive the marginal distribution of one variable, and the conditional distribution of the othe |
42,663 | Why need wald test ( a squared version of t test ) when we already have t test? | There are similarities between a Wald test and a t-test, as this page describes.* They both are used to determine whether a coefficient value is significantly different from 0 (or from any value specified in a null hypothesis). They typically arise in different contexts that show why we need both.
Consider the situatio... | Why need wald test ( a squared version of t test ) when we already have t test? | There are similarities between a Wald test and a t-test, as this page describes.* They both are used to determine whether a coefficient value is significantly different from 0 (or from any value speci | Why need wald test ( a squared version of t test ) when we already have t test?
There are similarities between a Wald test and a t-test, as this page describes.* They both are used to determine whether a coefficient value is significantly different from 0 (or from any value specified in a null hypothesis). They typical... | Why need wald test ( a squared version of t test ) when we already have t test?
There are similarities between a Wald test and a t-test, as this page describes.* They both are used to determine whether a coefficient value is significantly different from 0 (or from any value speci |
42,664 | What is the relation between ELBO and SGVB? | The evidence lower bound is a bound on the log probability of the data. But there is no straightforward way to compute the ELBO, since it requires taking an expectation over the variational posterior. Therefore we need a procedure to estimate the ELBO (more specifically we need some way to estimate the gradient of the ... | What is the relation between ELBO and SGVB? | The evidence lower bound is a bound on the log probability of the data. But there is no straightforward way to compute the ELBO, since it requires taking an expectation over the variational posterior. | What is the relation between ELBO and SGVB?
The evidence lower bound is a bound on the log probability of the data. But there is no straightforward way to compute the ELBO, since it requires taking an expectation over the variational posterior. Therefore we need a procedure to estimate the ELBO (more specifically we ne... | What is the relation between ELBO and SGVB?
The evidence lower bound is a bound on the log probability of the data. But there is no straightforward way to compute the ELBO, since it requires taking an expectation over the variational posterior. |
42,665 | conditional and interventional expectation | Yes, you can consider $X$ and $Z$ to be arbitrary vectors of variables. The identification problem of expressions of the type $E[Y|do(X)]$ and $E[Y|do(X), Z]$ for arbitrary vectors of variables $X$ and $Z$ has been solved for nonparametric models using the do-calculus (via the ID-algorithm).
For instance, in the model ... | conditional and interventional expectation | Yes, you can consider $X$ and $Z$ to be arbitrary vectors of variables. The identification problem of expressions of the type $E[Y|do(X)]$ and $E[Y|do(X), Z]$ for arbitrary vectors of variables $X$ an | conditional and interventional expectation
Yes, you can consider $X$ and $Z$ to be arbitrary vectors of variables. The identification problem of expressions of the type $E[Y|do(X)]$ and $E[Y|do(X), Z]$ for arbitrary vectors of variables $X$ and $Z$ has been solved for nonparametric models using the do-calculus (via the... | conditional and interventional expectation
Yes, you can consider $X$ and $Z$ to be arbitrary vectors of variables. The identification problem of expressions of the type $E[Y|do(X)]$ and $E[Y|do(X), Z]$ for arbitrary vectors of variables $X$ an |
42,666 | Pairwise comparisons of regression coefficients [duplicate] | Use the emmeans package, specifically pairs(emtrends(m, ~grp, var="var")) ... where grp is the categorical (grouping) variable, "var" is the slope variable.
library(emmeans)
m <- lm(Sepal.Width ~ Species*Sepal.Length, iris)
pairs(emtrends(m, ~Species, var="Sepal.Length"))
## contrast estimate SE df ... | Pairwise comparisons of regression coefficients [duplicate] | Use the emmeans package, specifically pairs(emtrends(m, ~grp, var="var")) ... where grp is the categorical (grouping) variable, "var" is the slope variable.
library(emmeans)
m <- lm(Sepal.Width ~ Spec | Pairwise comparisons of regression coefficients [duplicate]
Use the emmeans package, specifically pairs(emtrends(m, ~grp, var="var")) ... where grp is the categorical (grouping) variable, "var" is the slope variable.
library(emmeans)
m <- lm(Sepal.Width ~ Species*Sepal.Length, iris)
pairs(emtrends(m, ~Species, var="Sep... | Pairwise comparisons of regression coefficients [duplicate]
Use the emmeans package, specifically pairs(emtrends(m, ~grp, var="var")) ... where grp is the categorical (grouping) variable, "var" is the slope variable.
library(emmeans)
m <- lm(Sepal.Width ~ Spec |
42,667 | How to improve this time series model? | You politely asked "My question is about how to improve this model further . Because you will see that residuals are not normally distribute" . I have answered .....
I took your 290 monthly values (1987/1) and introduced them to AUTOBOX which automatically identified the following model.
1) ARIMA (1,1,0)(0,0,0)12 WITH... | How to improve this time series model? | You politely asked "My question is about how to improve this model further . Because you will see that residuals are not normally distribute" . I have answered .....
I took your 290 monthly values (19 | How to improve this time series model?
You politely asked "My question is about how to improve this model further . Because you will see that residuals are not normally distribute" . I have answered .....
I took your 290 monthly values (1987/1) and introduced them to AUTOBOX which automatically identified the following... | How to improve this time series model?
You politely asked "My question is about how to improve this model further . Because you will see that residuals are not normally distribute" . I have answered .....
I took your 290 monthly values (19 |
42,668 | Advantages of using t-value as test statistic in permutation tests? | The main reason to use a t-value (and, indeed, any approximately pivotal test statistic) in a permutation test is to give the test asymptotic validity in the case of unequal variances. Given that you always want this property, you should always base permutation tests on β/(SE β) rather than just β alone.
This property ... | Advantages of using t-value as test statistic in permutation tests? | The main reason to use a t-value (and, indeed, any approximately pivotal test statistic) in a permutation test is to give the test asymptotic validity in the case of unequal variances. Given that you | Advantages of using t-value as test statistic in permutation tests?
The main reason to use a t-value (and, indeed, any approximately pivotal test statistic) in a permutation test is to give the test asymptotic validity in the case of unequal variances. Given that you always want this property, you should always base pe... | Advantages of using t-value as test statistic in permutation tests?
The main reason to use a t-value (and, indeed, any approximately pivotal test statistic) in a permutation test is to give the test asymptotic validity in the case of unequal variances. Given that you |
42,669 | Cross validation : hyper-parameter tuning ? or model validation? | I'd say mostly the first (i.e. Hyper-parameter tuning).
If you have a sufficiently large hold-out test set you can evaluate the models pretty reliably. When wanting to select hyperparameters, having a validation set could cause your model to overfit on that. CV makes it much harder to do so. | Cross validation : hyper-parameter tuning ? or model validation? | I'd say mostly the first (i.e. Hyper-parameter tuning).
If you have a sufficiently large hold-out test set you can evaluate the models pretty reliably. When wanting to select hyperparameters, having a | Cross validation : hyper-parameter tuning ? or model validation?
I'd say mostly the first (i.e. Hyper-parameter tuning).
If you have a sufficiently large hold-out test set you can evaluate the models pretty reliably. When wanting to select hyperparameters, having a validation set could cause your model to overfit on th... | Cross validation : hyper-parameter tuning ? or model validation?
I'd say mostly the first (i.e. Hyper-parameter tuning).
If you have a sufficiently large hold-out test set you can evaluate the models pretty reliably. When wanting to select hyperparameters, having a |
42,670 | Cross validation : hyper-parameter tuning ? or model validation? | Model Selection and Model Hyperparams Tuning are conceptually equivalent.
If a model has a single Hyperparam, which in turn can have 3 possible values, it's like you have 3 different models. Very similar models maybe, but for the purpose of optimization they are treated as if they are 3, completely different and indep... | Cross validation : hyper-parameter tuning ? or model validation? | Model Selection and Model Hyperparams Tuning are conceptually equivalent.
If a model has a single Hyperparam, which in turn can have 3 possible values, it's like you have 3 different models. Very sim | Cross validation : hyper-parameter tuning ? or model validation?
Model Selection and Model Hyperparams Tuning are conceptually equivalent.
If a model has a single Hyperparam, which in turn can have 3 possible values, it's like you have 3 different models. Very similar models maybe, but for the purpose of optimization ... | Cross validation : hyper-parameter tuning ? or model validation?
Model Selection and Model Hyperparams Tuning are conceptually equivalent.
If a model has a single Hyperparam, which in turn can have 3 possible values, it's like you have 3 different models. Very sim |
42,671 | Cross validation : hyper-parameter tuning ? or model validation? | Suppose you have split your data into train-validation-test sets. You do not usually split it into train-test unless your models have no hyperparameters.
Validation set is always used to tune hyperparameters of your models. Test set is used to assess the final performance of your model, and compare different classes o... | Cross validation : hyper-parameter tuning ? or model validation? | Suppose you have split your data into train-validation-test sets. You do not usually split it into train-test unless your models have no hyperparameters.
Validation set is always used to tune hyperpa | Cross validation : hyper-parameter tuning ? or model validation?
Suppose you have split your data into train-validation-test sets. You do not usually split it into train-test unless your models have no hyperparameters.
Validation set is always used to tune hyperparameters of your models. Test set is used to assess the... | Cross validation : hyper-parameter tuning ? or model validation?
Suppose you have split your data into train-validation-test sets. You do not usually split it into train-test unless your models have no hyperparameters.
Validation set is always used to tune hyperpa |
42,672 | MLE of $f(x\mid\theta) = \theta x^{\theta−1}e^{−x^{\theta}}I_{(0,\infty)}(x)$ | If you're not sure whether or not your answer is correct, a useful check is to plot a graph of the log-likelihood function and see if your purported MLE looks visually to give the maximising value. I will do this below, but I include the mathematics for deriving the MLE in the general case.
MLE in the general case: F... | MLE of $f(x\mid\theta) = \theta x^{\theta−1}e^{−x^{\theta}}I_{(0,\infty)}(x)$ | If you're not sure whether or not your answer is correct, a useful check is to plot a graph of the log-likelihood function and see if your purported MLE looks visually to give the maximising value. I | MLE of $f(x\mid\theta) = \theta x^{\theta−1}e^{−x^{\theta}}I_{(0,\infty)}(x)$
If you're not sure whether or not your answer is correct, a useful check is to plot a graph of the log-likelihood function and see if your purported MLE looks visually to give the maximising value. I will do this below, but I include the mat... | MLE of $f(x\mid\theta) = \theta x^{\theta−1}e^{−x^{\theta}}I_{(0,\infty)}(x)$
If you're not sure whether or not your answer is correct, a useful check is to plot a graph of the log-likelihood function and see if your purported MLE looks visually to give the maximising value. I |
42,673 | What is this parameter estimation strategy called? | Your second estimator is the "plug-in" estimator, based on the invariance property of MLE's it is the maximum-likelihood estimator (under the normal assumptions). The first estimator could be called a moments estimator, but could also be seen as non-parametric, as it is unbiased without need for normality assumption.
... | What is this parameter estimation strategy called? | Your second estimator is the "plug-in" estimator, based on the invariance property of MLE's it is the maximum-likelihood estimator (under the normal assumptions). The first estimator could be called a | What is this parameter estimation strategy called?
Your second estimator is the "plug-in" estimator, based on the invariance property of MLE's it is the maximum-likelihood estimator (under the normal assumptions). The first estimator could be called a moments estimator, but could also be seen as non-parametric, as it i... | What is this parameter estimation strategy called?
Your second estimator is the "plug-in" estimator, based on the invariance property of MLE's it is the maximum-likelihood estimator (under the normal assumptions). The first estimator could be called a |
42,674 | Convolution for uniform distribution and standard normal distribution | You're making the substitution $x = z - u$ to transform the integral. The differential of this is:
$$ dx = 0 - du = - du $$
So the calculation finishes up like this:
$$=\int_{0}^{1}f_X(z-u)du = - \int_{z}^{z-1}f_X(x)dx = \int_{z-1}^{z}f_X(x)dx$$ | Convolution for uniform distribution and standard normal distribution | You're making the substitution $x = z - u$ to transform the integral. The differential of this is:
$$ dx = 0 - du = - du $$
So the calculation finishes up like this:
$$=\int_{0}^{1}f_X(z-u)du = - \in | Convolution for uniform distribution and standard normal distribution
You're making the substitution $x = z - u$ to transform the integral. The differential of this is:
$$ dx = 0 - du = - du $$
So the calculation finishes up like this:
$$=\int_{0}^{1}f_X(z-u)du = - \int_{z}^{z-1}f_X(x)dx = \int_{z-1}^{z}f_X(x)dx$$ | Convolution for uniform distribution and standard normal distribution
You're making the substitution $x = z - u$ to transform the integral. The differential of this is:
$$ dx = 0 - du = - du $$
So the calculation finishes up like this:
$$=\int_{0}^{1}f_X(z-u)du = - \in |
42,675 | Is convolution neural network (CNN) a special case of multilayer perceptron (MLP)? And why not use MLP for everything? | A convolution can be expressed as matrix multiplication but the matrix is multiplied with a patch around every position in the image separately. So you go to (1/1) and extract a patch and multiply it with an MLP. Then you do the same thing at position (1/2) and so forth. So obviously there are less degrees of freedom t... | Is convolution neural network (CNN) a special case of multilayer perceptron (MLP)? And why not use M | A convolution can be expressed as matrix multiplication but the matrix is multiplied with a patch around every position in the image separately. So you go to (1/1) and extract a patch and multiply it | Is convolution neural network (CNN) a special case of multilayer perceptron (MLP)? And why not use MLP for everything?
A convolution can be expressed as matrix multiplication but the matrix is multiplied with a patch around every position in the image separately. So you go to (1/1) and extract a patch and multiply it w... | Is convolution neural network (CNN) a special case of multilayer perceptron (MLP)? And why not use M
A convolution can be expressed as matrix multiplication but the matrix is multiplied with a patch around every position in the image separately. So you go to (1/1) and extract a patch and multiply it |
42,676 | Puzzling predicted values in generalized multilevel model | The answer might at least partly be hidden in your answer:
The predicted random effects are all non-negative. Adding them to the linear predictor of the fixed effects will push up the average prediction to the desired level.
So what could be the reason for the non-centered distribution of the eBLUPs? I think it is hid... | Puzzling predicted values in generalized multilevel model | The answer might at least partly be hidden in your answer:
The predicted random effects are all non-negative. Adding them to the linear predictor of the fixed effects will push up the average predicti | Puzzling predicted values in generalized multilevel model
The answer might at least partly be hidden in your answer:
The predicted random effects are all non-negative. Adding them to the linear predictor of the fixed effects will push up the average prediction to the desired level.
So what could be the reason for the ... | Puzzling predicted values in generalized multilevel model
The answer might at least partly be hidden in your answer:
The predicted random effects are all non-negative. Adding them to the linear predictor of the fixed effects will push up the average predicti |
42,677 | Which method is correct? (generalized additive model, mgcv) | I would first try an ANOVA and look at that F-test. If it rejects that, there is evidence that there is variation between treatments. After that, since your outcome if count data, I would do a poisson regression. Unlike GAMs where it can be hard to test the important of effects, Poisson regression gives you your usuall... | Which method is correct? (generalized additive model, mgcv) | I would first try an ANOVA and look at that F-test. If it rejects that, there is evidence that there is variation between treatments. After that, since your outcome if count data, I would do a poisson | Which method is correct? (generalized additive model, mgcv)
I would first try an ANOVA and look at that F-test. If it rejects that, there is evidence that there is variation between treatments. After that, since your outcome if count data, I would do a poisson regression. Unlike GAMs where it can be hard to test the im... | Which method is correct? (generalized additive model, mgcv)
I would first try an ANOVA and look at that F-test. If it rejects that, there is evidence that there is variation between treatments. After that, since your outcome if count data, I would do a poisson |
42,678 | Which method is correct? (generalized additive model, mgcv) | I'd guess you want the by parameter for s. The resulting set of plots then has one for each level of the by factor. In this example from ?gam.models, there are three levels of fac, so there are three plots for x2 and one for x0.
dat <- gamSim(4)
b <- gam(y ~ fac + s(x2, by=fac) + s(x0), data=dat)
plot(b, pages=1) | Which method is correct? (generalized additive model, mgcv) | I'd guess you want the by parameter for s. The resulting set of plots then has one for each level of the by factor. In this example from ?gam.models, there are three levels of fac, so there are thre | Which method is correct? (generalized additive model, mgcv)
I'd guess you want the by parameter for s. The resulting set of plots then has one for each level of the by factor. In this example from ?gam.models, there are three levels of fac, so there are three plots for x2 and one for x0.
dat <- gamSim(4)
b <- gam(y ~... | Which method is correct? (generalized additive model, mgcv)
I'd guess you want the by parameter for s. The resulting set of plots then has one for each level of the by factor. In this example from ?gam.models, there are three levels of fac, so there are thre |
42,679 | Which method is correct? (generalized additive model, mgcv) | You have some problems with your data; you can't have a random effect of Plant as a factor and get the plot you showed in the link in comment to @Aaron's Answer. Are you sure that Plant is a factor with more than 1 level? If not, you need to code your data correctly to get a random intercept per plant. Also, can you in... | Which method is correct? (generalized additive model, mgcv) | You have some problems with your data; you can't have a random effect of Plant as a factor and get the plot you showed in the link in comment to @Aaron's Answer. Are you sure that Plant is a factor wi | Which method is correct? (generalized additive model, mgcv)
You have some problems with your data; you can't have a random effect of Plant as a factor and get the plot you showed in the link in comment to @Aaron's Answer. Are you sure that Plant is a factor with more than 1 level? If not, you need to code your data cor... | Which method is correct? (generalized additive model, mgcv)
You have some problems with your data; you can't have a random effect of Plant as a factor and get the plot you showed in the link in comment to @Aaron's Answer. Are you sure that Plant is a factor wi |
42,680 | Principal component analysis on time series : meaning? | If I understand correctly, your question is about the reason to use MSSA for a system of time series, if one can apply PCA (or SVD) to this system.
The general answer is that the result of PCA is mostly an unstructured approximation (I mean from the viewpoint of the temporal structure), while SSA takes into considerati... | Principal component analysis on time series : meaning? | If I understand correctly, your question is about the reason to use MSSA for a system of time series, if one can apply PCA (or SVD) to this system.
The general answer is that the result of PCA is most | Principal component analysis on time series : meaning?
If I understand correctly, your question is about the reason to use MSSA for a system of time series, if one can apply PCA (or SVD) to this system.
The general answer is that the result of PCA is mostly an unstructured approximation (I mean from the viewpoint of th... | Principal component analysis on time series : meaning?
If I understand correctly, your question is about the reason to use MSSA for a system of time series, if one can apply PCA (or SVD) to this system.
The general answer is that the result of PCA is most |
42,681 | Confidence bounds for an ECDF | In Matlab's console type:
edit ecdf
It opens the source code in the editor.
Go to line 194:
if nargout>2 || (nargout==0 && isequal(bounds,'on'))
This is the start of the code block that calculates the lower - and upper (confidence) bounds: [Flo, Fup]. The code block is 30 lines long and pretty straightforward. Posted... | Confidence bounds for an ECDF | In Matlab's console type:
edit ecdf
It opens the source code in the editor.
Go to line 194:
if nargout>2 || (nargout==0 && isequal(bounds,'on'))
This is the start of the code block that calculates t | Confidence bounds for an ECDF
In Matlab's console type:
edit ecdf
It opens the source code in the editor.
Go to line 194:
if nargout>2 || (nargout==0 && isequal(bounds,'on'))
This is the start of the code block that calculates the lower - and upper (confidence) bounds: [Flo, Fup]. The code block is 30 lines long and ... | Confidence bounds for an ECDF
In Matlab's console type:
edit ecdf
It opens the source code in the editor.
Go to line 194:
if nargout>2 || (nargout==0 && isequal(bounds,'on'))
This is the start of the code block that calculates t |
42,682 | Correct feature aggregation for this tricky buying problem | 0) and 2) Moving Average Models. Suppose we are given nothing else than the following time series data
time y
1: 0 -12.070657
2: 1 4.658008
3: 2 14.604409
4: 3 -17.835538
5: 4 11.751944
...
which looks like this:
for the purpose of this toy example it is essentially sin(ti... | Correct feature aggregation for this tricky buying problem | 0) and 2) Moving Average Models. Suppose we are given nothing else than the following time series data
time y
1: 0 -12.070657
2: 1 4.658008
3: 2 14.604409
4: 3 -17 | Correct feature aggregation for this tricky buying problem
0) and 2) Moving Average Models. Suppose we are given nothing else than the following time series data
time y
1: 0 -12.070657
2: 1 4.658008
3: 2 14.604409
4: 3 -17.835538
5: 4 11.751944
...
which looks like this:
f... | Correct feature aggregation for this tricky buying problem
0) and 2) Moving Average Models. Suppose we are given nothing else than the following time series data
time y
1: 0 -12.070657
2: 1 4.658008
3: 2 14.604409
4: 3 -17 |
42,683 | Train/Test Splitting for Time Series | Product demand data usually has a yearly seasonality.
Training on the first year is not sufficient, as your model won't be able to capture any yearly seasonality or any long term trends. Most algorithms require at least 2 years of data for this reason (more would be better - but that's not always available for retail ... | Train/Test Splitting for Time Series | Product demand data usually has a yearly seasonality.
Training on the first year is not sufficient, as your model won't be able to capture any yearly seasonality or any long term trends. Most algorit | Train/Test Splitting for Time Series
Product demand data usually has a yearly seasonality.
Training on the first year is not sufficient, as your model won't be able to capture any yearly seasonality or any long term trends. Most algorithms require at least 2 years of data for this reason (more would be better - but th... | Train/Test Splitting for Time Series
Product demand data usually has a yearly seasonality.
Training on the first year is not sufficient, as your model won't be able to capture any yearly seasonality or any long term trends. Most algorit |
42,684 | Train/Test Splitting for Time Series | Do you believe there is significant year to year variation?
If yes, it probably doesn't make sense to fit the model with only 1 year of data & a source of variation removed.
If no, which is generally unlikely due to seasonal trends, you might try it with 1 year of data. Using this small of a train set is a bit uncommo... | Train/Test Splitting for Time Series | Do you believe there is significant year to year variation?
If yes, it probably doesn't make sense to fit the model with only 1 year of data & a source of variation removed.
If no, which is generally | Train/Test Splitting for Time Series
Do you believe there is significant year to year variation?
If yes, it probably doesn't make sense to fit the model with only 1 year of data & a source of variation removed.
If no, which is generally unlikely due to seasonal trends, you might try it with 1 year of data. Using this ... | Train/Test Splitting for Time Series
Do you believe there is significant year to year variation?
If yes, it probably doesn't make sense to fit the model with only 1 year of data & a source of variation removed.
If no, which is generally |
42,685 | Train/Test Splitting for Time Series | Daily data is frequently heavily dependent on daily habits i.e. it is more important to take into account deterministic structure while dealing with memory effect (same day last week for example ) . Daily data is also dependent on holiday effects (lead , contemporaneous and lags). In addition there are often monthly ef... | Train/Test Splitting for Time Series | Daily data is frequently heavily dependent on daily habits i.e. it is more important to take into account deterministic structure while dealing with memory effect (same day last week for example ) . D | Train/Test Splitting for Time Series
Daily data is frequently heavily dependent on daily habits i.e. it is more important to take into account deterministic structure while dealing with memory effect (same day last week for example ) . Daily data is also dependent on holiday effects (lead , contemporaneous and lags). I... | Train/Test Splitting for Time Series
Daily data is frequently heavily dependent on daily habits i.e. it is more important to take into account deterministic structure while dealing with memory effect (same day last week for example ) . D |
42,686 | Linear regression model is under-predicting | I think it can be one of two things (I would have to take a look at your data to say for sure):
either your data has high homoskedasticity
or your data is strongly auto-correlated (a typical characteristic of time series) | Linear regression model is under-predicting | I think it can be one of two things (I would have to take a look at your data to say for sure):
either your data has high homoskedasticity
or your data is strongly auto-correlated (a typical characte | Linear regression model is under-predicting
I think it can be one of two things (I would have to take a look at your data to say for sure):
either your data has high homoskedasticity
or your data is strongly auto-correlated (a typical characteristic of time series) | Linear regression model is under-predicting
I think it can be one of two things (I would have to take a look at your data to say for sure):
either your data has high homoskedasticity
or your data is strongly auto-correlated (a typical characte |
42,687 | Turkish speech recognition (speech->text) in Google Speech API? [closed] | What is used in production is often not disclosed. I'm not aware of Google disclosing how the current automated speech recognition (ASR) system they using production works. One way to approximate it would be to scan ICASSP/Interspeech/etc. proceedings for Google publications.
Anyway, putting Google aside: the question ... | Turkish speech recognition (speech->text) in Google Speech API? [closed] | What is used in production is often not disclosed. I'm not aware of Google disclosing how the current automated speech recognition (ASR) system they using production works. One way to approximate it w | Turkish speech recognition (speech->text) in Google Speech API? [closed]
What is used in production is often not disclosed. I'm not aware of Google disclosing how the current automated speech recognition (ASR) system they using production works. One way to approximate it would be to scan ICASSP/Interspeech/etc. proceed... | Turkish speech recognition (speech->text) in Google Speech API? [closed]
What is used in production is often not disclosed. I'm not aware of Google disclosing how the current automated speech recognition (ASR) system they using production works. One way to approximate it w |
42,688 | definition of "hidden unit" in a ConvNet | Generally speaking, I think for conv layers we tend not to focus on the concept of 'hidden unit', but to get it out of the way, when I think 'hidden unit', I think of the concepts of 'hidden' and 'unit'. For me, 'hidden' means it's neither something in the input layer (the inputs to the network), or the output layer (t... | definition of "hidden unit" in a ConvNet | Generally speaking, I think for conv layers we tend not to focus on the concept of 'hidden unit', but to get it out of the way, when I think 'hidden unit', I think of the concepts of 'hidden' and 'uni | definition of "hidden unit" in a ConvNet
Generally speaking, I think for conv layers we tend not to focus on the concept of 'hidden unit', but to get it out of the way, when I think 'hidden unit', I think of the concepts of 'hidden' and 'unit'. For me, 'hidden' means it's neither something in the input layer (the input... | definition of "hidden unit" in a ConvNet
Generally speaking, I think for conv layers we tend not to focus on the concept of 'hidden unit', but to get it out of the way, when I think 'hidden unit', I think of the concepts of 'hidden' and 'uni |
42,689 | definition of "hidden unit" in a ConvNet | I think @stephen & @hugh have made it over-complicated,
let's make it simple.
A hidden unit, in general, has an operation Activation(W*X+b). Therefore, if you think carefully,
*A hidden unit in CONV layer is an operation that uses "filter_volume a.k.a volume of randomly initialized weights" in general. More loosely,... | definition of "hidden unit" in a ConvNet | I think @stephen & @hugh have made it over-complicated,
let's make it simple.
A hidden unit, in general, has an operation Activation(W*X+b). Therefore, if you think carefully,
*A hidden unit in CON | definition of "hidden unit" in a ConvNet
I think @stephen & @hugh have made it over-complicated,
let's make it simple.
A hidden unit, in general, has an operation Activation(W*X+b). Therefore, if you think carefully,
*A hidden unit in CONV layer is an operation that uses "filter_volume a.k.a volume of randomly initi... | definition of "hidden unit" in a ConvNet
I think @stephen & @hugh have made it over-complicated,
let's make it simple.
A hidden unit, in general, has an operation Activation(W*X+b). Therefore, if you think carefully,
*A hidden unit in CON |
42,690 | definition of "hidden unit" in a ConvNet | I don't think either of the answers provides a clear definition, so I will attempt to answer it because I stumbled into the same problem finding a clear definition of a hidden unit in the context of a Convolutional Neural Network.
Hidden units in this context are the feature maps or filters. So for Tensorflow or Kera... | definition of "hidden unit" in a ConvNet | I don't think either of the answers provides a clear definition, so I will attempt to answer it because I stumbled into the same problem finding a clear definition of a hidden unit in the context of a | definition of "hidden unit" in a ConvNet
I don't think either of the answers provides a clear definition, so I will attempt to answer it because I stumbled into the same problem finding a clear definition of a hidden unit in the context of a Convolutional Neural Network.
Hidden units in this context are the feature m... | definition of "hidden unit" in a ConvNet
I don't think either of the answers provides a clear definition, so I will attempt to answer it because I stumbled into the same problem finding a clear definition of a hidden unit in the context of a |
42,691 | definition of "hidden unit" in a ConvNet | There are so many complexities in this topic which may confuse one so let's break it down.
Here is what we have,
(Layerl1) (9 * 9 * 3) ---->Conv with(3 * 3 * 3),5 filters with s=1 p=0-----> (layerl2) (7 * 7 * 5)
now,
unit:- A units in a layer is that whoose receptive fields cover a patch of the previous layer
and,
no ... | definition of "hidden unit" in a ConvNet | There are so many complexities in this topic which may confuse one so let's break it down.
Here is what we have,
(Layerl1) (9 * 9 * 3) ---->Conv with(3 * 3 * 3),5 filters with s=1 p=0-----> (layerl2) | definition of "hidden unit" in a ConvNet
There are so many complexities in this topic which may confuse one so let's break it down.
Here is what we have,
(Layerl1) (9 * 9 * 3) ---->Conv with(3 * 3 * 3),5 filters with s=1 p=0-----> (layerl2) (7 * 7 * 5)
now,
unit:- A units in a layer is that whoose receptive fields cove... | definition of "hidden unit" in a ConvNet
There are so many complexities in this topic which may confuse one so let's break it down.
Here is what we have,
(Layerl1) (9 * 9 * 3) ---->Conv with(3 * 3 * 3),5 filters with s=1 p=0-----> (layerl2) |
42,692 | What are the relation and differences between time series and linear regression? | In the context of Statistics, linear regression is solved by maximizing the likliehood that the error of a model linear in basis is the mean of a Normal Distribution. During maximization we assume the observations are independently and identically distributed, clearly not a reasonable assumption for times series data. | What are the relation and differences between time series and linear regression? | In the context of Statistics, linear regression is solved by maximizing the likliehood that the error of a model linear in basis is the mean of a Normal Distribution. During maximization we assume the | What are the relation and differences between time series and linear regression?
In the context of Statistics, linear regression is solved by maximizing the likliehood that the error of a model linear in basis is the mean of a Normal Distribution. During maximization we assume the observations are independently and ide... | What are the relation and differences between time series and linear regression?
In the context of Statistics, linear regression is solved by maximizing the likliehood that the error of a model linear in basis is the mean of a Normal Distribution. During maximization we assume the |
42,693 | What are the relation and differences between time series and linear regression? | From Ordinary Regression to Time Series Regression:
The time series regression model is an extension of the ordinary regression model in which the following conditions exist:
Variables are observed in time.
Autocorrelation is allowed.
The target variable can be influenced by past values of inputs.
Source: DePa... | What are the relation and differences between time series and linear regression? | From Ordinary Regression to Time Series Regression:
The time series regression model is an extension of the ordinary regression model in which the following conditions exist:
Variables are observed | What are the relation and differences between time series and linear regression?
From Ordinary Regression to Time Series Regression:
The time series regression model is an extension of the ordinary regression model in which the following conditions exist:
Variables are observed in time.
Autocorrelation is allowed... | What are the relation and differences between time series and linear regression?
From Ordinary Regression to Time Series Regression:
The time series regression model is an extension of the ordinary regression model in which the following conditions exist:
Variables are observed |
42,694 | Step-wise Bayesian updating as a prior selection strategy | "Yesterday’s posterior is today’s prior" is the best Bayesian learning strategy if you know with absolute certainty that "Today's parameter is yesterday's parameter".
is it possible to use a wide prior for the oldest study, and then use
its posterior as prior for the study after it and so on to arrive at
one final... | Step-wise Bayesian updating as a prior selection strategy | "Yesterday’s posterior is today’s prior" is the best Bayesian learning strategy if you know with absolute certainty that "Today's parameter is yesterday's parameter".
is it possible to use a wide pri | Step-wise Bayesian updating as a prior selection strategy
"Yesterday’s posterior is today’s prior" is the best Bayesian learning strategy if you know with absolute certainty that "Today's parameter is yesterday's parameter".
is it possible to use a wide prior for the oldest study, and then use
its posterior as prior... | Step-wise Bayesian updating as a prior selection strategy
"Yesterday’s posterior is today’s prior" is the best Bayesian learning strategy if you know with absolute certainty that "Today's parameter is yesterday's parameter".
is it possible to use a wide pri |
42,695 | Step-wise Bayesian updating as a prior selection strategy | There has been some work on deriving priors from previous studies. Two relevant papers may be "Summarizing historical information on controls in clinical trials" by Neuenschwander and colleagues available from Clinical Trials and "Robust meta-analytic-predictive priors in clinical trials with historical control informa... | Step-wise Bayesian updating as a prior selection strategy | There has been some work on deriving priors from previous studies. Two relevant papers may be "Summarizing historical information on controls in clinical trials" by Neuenschwander and colleagues avail | Step-wise Bayesian updating as a prior selection strategy
There has been some work on deriving priors from previous studies. Two relevant papers may be "Summarizing historical information on controls in clinical trials" by Neuenschwander and colleagues available from Clinical Trials and "Robust meta-analytic-predictive... | Step-wise Bayesian updating as a prior selection strategy
There has been some work on deriving priors from previous studies. Two relevant papers may be "Summarizing historical information on controls in clinical trials" by Neuenschwander and colleagues avail |
42,696 | Should missing observations be included in the number of observations if correcting for multiple testing | If the missing value causes the observation not to be included in the calculations of parameter estimates, it's contributing nothing to the end result (for better or worse) and should not be included in the p-value or adjusted p-value calculation. Its effect is the same as if it hadn't been included in the data set at... | Should missing observations be included in the number of observations if correcting for multiple tes | If the missing value causes the observation not to be included in the calculations of parameter estimates, it's contributing nothing to the end result (for better or worse) and should not be included | Should missing observations be included in the number of observations if correcting for multiple testing
If the missing value causes the observation not to be included in the calculations of parameter estimates, it's contributing nothing to the end result (for better or worse) and should not be included in the p-value ... | Should missing observations be included in the number of observations if correcting for multiple tes
If the missing value causes the observation not to be included in the calculations of parameter estimates, it's contributing nothing to the end result (for better or worse) and should not be included |
42,697 | Frequentist Predictive Distribution for a Cauchy variable | The general solution to your problem is Maximum Likelihood Estimation (MLE) of your parameters $\theta$. Once they are obtained as $\hat{\theta}$, you substitute them into your pdf for the unknown parameters, i.e. you estimate the pdf of your random variable as $\hat{f}(x_i) = f(x_i|\hat{\theta})$. This allows you to ... | Frequentist Predictive Distribution for a Cauchy variable | The general solution to your problem is Maximum Likelihood Estimation (MLE) of your parameters $\theta$. Once they are obtained as $\hat{\theta}$, you substitute them into your pdf for the unknown pa | Frequentist Predictive Distribution for a Cauchy variable
The general solution to your problem is Maximum Likelihood Estimation (MLE) of your parameters $\theta$. Once they are obtained as $\hat{\theta}$, you substitute them into your pdf for the unknown parameters, i.e. you estimate the pdf of your random variable as... | Frequentist Predictive Distribution for a Cauchy variable
The general solution to your problem is Maximum Likelihood Estimation (MLE) of your parameters $\theta$. Once they are obtained as $\hat{\theta}$, you substitute them into your pdf for the unknown pa |
42,698 | Frequentist Predictive Distribution for a Cauchy variable | One could use a Monte Carlo method to obtain empirical estimates for relationships between the $x_1....x_i$ and the prediction interval for $x_{i+n}$.
Motivation: If we estimate the prediction interval based on the quartiles/CDF of a distribution that follows from maximum likelihood estimates (or other type of paramete... | Frequentist Predictive Distribution for a Cauchy variable | One could use a Monte Carlo method to obtain empirical estimates for relationships between the $x_1....x_i$ and the prediction interval for $x_{i+n}$.
Motivation: If we estimate the prediction interva | Frequentist Predictive Distribution for a Cauchy variable
One could use a Monte Carlo method to obtain empirical estimates for relationships between the $x_1....x_i$ and the prediction interval for $x_{i+n}$.
Motivation: If we estimate the prediction interval based on the quartiles/CDF of a distribution that follows fr... | Frequentist Predictive Distribution for a Cauchy variable
One could use a Monte Carlo method to obtain empirical estimates for relationships between the $x_1....x_i$ and the prediction interval for $x_{i+n}$.
Motivation: If we estimate the prediction interva |
42,699 | Frequentist Predictive Distribution for a Cauchy variable | It seems that all you need is to estimate the parameters of Cauchy distribution from the dataset $x_i$. Here's what Stephens proposes, it's not MLE, and author claims this method is consistent and more stable than MLE though you have to take into account that this has been written in the last century.
where Cauchy is ... | Frequentist Predictive Distribution for a Cauchy variable | It seems that all you need is to estimate the parameters of Cauchy distribution from the dataset $x_i$. Here's what Stephens proposes, it's not MLE, and author claims this method is consistent and mor | Frequentist Predictive Distribution for a Cauchy variable
It seems that all you need is to estimate the parameters of Cauchy distribution from the dataset $x_i$. Here's what Stephens proposes, it's not MLE, and author claims this method is consistent and more stable than MLE though you have to take into account that th... | Frequentist Predictive Distribution for a Cauchy variable
It seems that all you need is to estimate the parameters of Cauchy distribution from the dataset $x_i$. Here's what Stephens proposes, it's not MLE, and author claims this method is consistent and mor |
42,700 | Why auto.arima does not differentiate when there is xreg? | There is a test inside the forecast function for whether the series should be differenced:
if (is.na(d)) {
d <- ndiffs(dx, test = test, max.d = max.d)
if (d > 0 & !is.null(xregg)) {
diffxreg <- diff(diffxreg, differences = d, lag = 1)
if (any(apply(diffxreg, 2, is.constant)))
d <- d... | Why auto.arima does not differentiate when there is xreg? | There is a test inside the forecast function for whether the series should be differenced:
if (is.na(d)) {
d <- ndiffs(dx, test = test, max.d = max.d)
if (d > 0 & !is.null(xregg)) {
di | Why auto.arima does not differentiate when there is xreg?
There is a test inside the forecast function for whether the series should be differenced:
if (is.na(d)) {
d <- ndiffs(dx, test = test, max.d = max.d)
if (d > 0 & !is.null(xregg)) {
diffxreg <- diff(diffxreg, differences = d, lag = 1)
if ... | Why auto.arima does not differentiate when there is xreg?
There is a test inside the forecast function for whether the series should be differenced:
if (is.na(d)) {
d <- ndiffs(dx, test = test, max.d = max.d)
if (d > 0 & !is.null(xregg)) {
di |
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