idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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43,201 | Conditional/normalized multiple imputation | Here's what Ian White, one of the contributors to Stata's original multiple imputation ice package, suggested:
I assume you believe that the distribution of Y|X "if Z were 1" is equal to the distribution of Y|X in the subgroup with observed Z equal to 1.
I think you can do this as follows.
Impute in the usual way e.g... | Conditional/normalized multiple imputation | Here's what Ian White, one of the contributors to Stata's original multiple imputation ice package, suggested:
I assume you believe that the distribution of Y|X "if Z were 1" is equal to the distribu | Conditional/normalized multiple imputation
Here's what Ian White, one of the contributors to Stata's original multiple imputation ice package, suggested:
I assume you believe that the distribution of Y|X "if Z were 1" is equal to the distribution of Y|X in the subgroup with observed Z equal to 1.
I think you can do th... | Conditional/normalized multiple imputation
Here's what Ian White, one of the contributors to Stata's original multiple imputation ice package, suggested:
I assume you believe that the distribution of Y|X "if Z were 1" is equal to the distribu |
43,202 | Conditional/normalized multiple imputation | I think one option would follow by analogy of predict on newdata in R. This supposes using mice to single-impute and then access the final regression model after burn in and convergence. This model is then used to make a one-time prediction as in predict.glm where newdata is a data set in which $Z=1$ has been replaced ... | Conditional/normalized multiple imputation | I think one option would follow by analogy of predict on newdata in R. This supposes using mice to single-impute and then access the final regression model after burn in and convergence. This model is | Conditional/normalized multiple imputation
I think one option would follow by analogy of predict on newdata in R. This supposes using mice to single-impute and then access the final regression model after burn in and convergence. This model is then used to make a one-time prediction as in predict.glm where newdata is a... | Conditional/normalized multiple imputation
I think one option would follow by analogy of predict on newdata in R. This supposes using mice to single-impute and then access the final regression model after burn in and convergence. This model is |
43,203 | SEM: Collinearity between two latent variables that are used to predict a third latent variable | Rules of thumb may say that multicollinearity is a problem only if two variables correlate above, say, .9 or even more. If two of your latent variables correlated that much, or even in the range of .7 / .8, then you have a problem before it comes to predicting the third variable: Your measurement model seems to be not ... | SEM: Collinearity between two latent variables that are used to predict a third latent variable | Rules of thumb may say that multicollinearity is a problem only if two variables correlate above, say, .9 or even more. If two of your latent variables correlated that much, or even in the range of .7 | SEM: Collinearity between two latent variables that are used to predict a third latent variable
Rules of thumb may say that multicollinearity is a problem only if two variables correlate above, say, .9 or even more. If two of your latent variables correlated that much, or even in the range of .7 / .8, then you have a p... | SEM: Collinearity between two latent variables that are used to predict a third latent variable
Rules of thumb may say that multicollinearity is a problem only if two variables correlate above, say, .9 or even more. If two of your latent variables correlated that much, or even in the range of .7 |
43,204 | SEM: Collinearity between two latent variables that are used to predict a third latent variable | Latent variable models are simply used to attempt to estimate the underlying constructs more reliably than by simply aggregating the items. Thus, in the structural part of the model (i.e. the regression) the same issues apply as in a standard regression.
Apart from in extreme situations (e.g., a standardized regression... | SEM: Collinearity between two latent variables that are used to predict a third latent variable | Latent variable models are simply used to attempt to estimate the underlying constructs more reliably than by simply aggregating the items. Thus, in the structural part of the model (i.e. the regressi | SEM: Collinearity between two latent variables that are used to predict a third latent variable
Latent variable models are simply used to attempt to estimate the underlying constructs more reliably than by simply aggregating the items. Thus, in the structural part of the model (i.e. the regression) the same issues appl... | SEM: Collinearity between two latent variables that are used to predict a third latent variable
Latent variable models are simply used to attempt to estimate the underlying constructs more reliably than by simply aggregating the items. Thus, in the structural part of the model (i.e. the regressi |
43,205 | What can be concluded from small sample size? | I didn't check your calculations but finding widely different standard deviations/sample size estimates from very small pilot studies is not surprising. Just like the sample mean, the sample standard deviation is a noisy estimate of its population counterpart. That's why it's not such a good idea to use a small pilot s... | What can be concluded from small sample size? | I didn't check your calculations but finding widely different standard deviations/sample size estimates from very small pilot studies is not surprising. Just like the sample mean, the sample standard | What can be concluded from small sample size?
I didn't check your calculations but finding widely different standard deviations/sample size estimates from very small pilot studies is not surprising. Just like the sample mean, the sample standard deviation is a noisy estimate of its population counterpart. That's why it... | What can be concluded from small sample size?
I didn't check your calculations but finding widely different standard deviations/sample size estimates from very small pilot studies is not surprising. Just like the sample mean, the sample standard |
43,206 | identifying events (patterns) that occur before an event of interest using sequence of events data | I suppose you have some large training set available.
This problem can be tackled with many different approaches and usually there is a trade-off between how well you can interpret findings/model and how good predictions you can make. I made something similar recently and after having a complex non-linear classifier t... | identifying events (patterns) that occur before an event of interest using sequence of events data | I suppose you have some large training set available.
This problem can be tackled with many different approaches and usually there is a trade-off between how well you can interpret findings/model and | identifying events (patterns) that occur before an event of interest using sequence of events data
I suppose you have some large training set available.
This problem can be tackled with many different approaches and usually there is a trade-off between how well you can interpret findings/model and how good predictions... | identifying events (patterns) that occur before an event of interest using sequence of events data
I suppose you have some large training set available.
This problem can be tackled with many different approaches and usually there is a trade-off between how well you can interpret findings/model and |
43,207 | Combine several softmax output probabilities | I think I may have found what I was looking for when I originally asked this question. A bit of googlefoo has led me to Linear Opinion Pools and variations thereof. Several papers are available here, here, here, here, here and finally here.
If any forum members have anything else to add, it would be appreciated. | Combine several softmax output probabilities | I think I may have found what I was looking for when I originally asked this question. A bit of googlefoo has led me to Linear Opinion Pools and variations thereof. Several papers are available here, | Combine several softmax output probabilities
I think I may have found what I was looking for when I originally asked this question. A bit of googlefoo has led me to Linear Opinion Pools and variations thereof. Several papers are available here, here, here, here, here and finally here.
If any forum members have anything... | Combine several softmax output probabilities
I think I may have found what I was looking for when I originally asked this question. A bit of googlefoo has led me to Linear Opinion Pools and variations thereof. Several papers are available here, |
43,208 | Hypothesis test for the presence of a Gaussian signal in i.i.d additive Gaussian noise | You can use Grubb's test here
Your statistical problem is essentially to test for a single "aberration" in your data vector. This is a very similar problem to using Grubb's test to detect an outlier. Indeed, one could reasonably say that it is the same problem. An obvious thing to do here is to test using Grubb's st... | Hypothesis test for the presence of a Gaussian signal in i.i.d additive Gaussian noise | You can use Grubb's test here
Your statistical problem is essentially to test for a single "aberration" in your data vector. This is a very similar problem to using Grubb's test to detect an outlier. | Hypothesis test for the presence of a Gaussian signal in i.i.d additive Gaussian noise
You can use Grubb's test here
Your statistical problem is essentially to test for a single "aberration" in your data vector. This is a very similar problem to using Grubb's test to detect an outlier. Indeed, one could reasonably sa... | Hypothesis test for the presence of a Gaussian signal in i.i.d additive Gaussian noise
You can use Grubb's test here
Your statistical problem is essentially to test for a single "aberration" in your data vector. This is a very similar problem to using Grubb's test to detect an outlier. |
43,209 | Statistics of sample correlation for uniformly distributed samples | Your question is indeed asking for the finite sample distribution
of $r_{N}$. To address your question, let me rephrase it in terms
of linear regressions. So a linkage between $r_{N}$ and the ordinary
least square (OLS) estimator could be highlighted. You observe two
variables $\left\{ x_{i}\right\} _{i=1}^{N}$ and $\l... | Statistics of sample correlation for uniformly distributed samples | Your question is indeed asking for the finite sample distribution
of $r_{N}$. To address your question, let me rephrase it in terms
of linear regressions. So a linkage between $r_{N}$ and the ordinary | Statistics of sample correlation for uniformly distributed samples
Your question is indeed asking for the finite sample distribution
of $r_{N}$. To address your question, let me rephrase it in terms
of linear regressions. So a linkage between $r_{N}$ and the ordinary
least square (OLS) estimator could be highlighted. Y... | Statistics of sample correlation for uniformly distributed samples
Your question is indeed asking for the finite sample distribution
of $r_{N}$. To address your question, let me rephrase it in terms
of linear regressions. So a linkage between $r_{N}$ and the ordinary |
43,210 | Why do structure learning for Bayesian networks? | Great question! From what I've seen, folks usually do inference given the structure and assume the structure is a given. I haven't seen folks do structure learning (which is a hard problem as you and others have pointed out) just for doing inference.
Bayesian networks encode conditional independence structure, so lea... | Why do structure learning for Bayesian networks? | Great question! From what I've seen, folks usually do inference given the structure and assume the structure is a given. I haven't seen folks do structure learning (which is a hard problem as you an | Why do structure learning for Bayesian networks?
Great question! From what I've seen, folks usually do inference given the structure and assume the structure is a given. I haven't seen folks do structure learning (which is a hard problem as you and others have pointed out) just for doing inference.
Bayesian networks ... | Why do structure learning for Bayesian networks?
Great question! From what I've seen, folks usually do inference given the structure and assume the structure is a given. I haven't seen folks do structure learning (which is a hard problem as you an |
43,211 | Why do structure learning for Bayesian networks? | In a Bayesian Belief Network (BBN), the joint probability can be decomposed. Assume the following.
U = {X1, X2, X3, X4 }, U is a set of variables
P(U) = P(X1, X2, X3, X4), P is the joint probability
Using the chain rule, you can decompose the P as follows
$P(U) = P(X1, X2, X3, X4) = P(X1)P(X2|X1)P(X3|X1,X2)P(X4|X1,X2... | Why do structure learning for Bayesian networks? | In a Bayesian Belief Network (BBN), the joint probability can be decomposed. Assume the following.
U = {X1, X2, X3, X4 }, U is a set of variables
P(U) = P(X1, X2, X3, X4), P is the joint probability
| Why do structure learning for Bayesian networks?
In a Bayesian Belief Network (BBN), the joint probability can be decomposed. Assume the following.
U = {X1, X2, X3, X4 }, U is a set of variables
P(U) = P(X1, X2, X3, X4), P is the joint probability
Using the chain rule, you can decompose the P as follows
$P(U) = P(X1,... | Why do structure learning for Bayesian networks?
In a Bayesian Belief Network (BBN), the joint probability can be decomposed. Assume the following.
U = {X1, X2, X3, X4 }, U is a set of variables
P(U) = P(X1, X2, X3, X4), P is the joint probability
|
43,212 | Knapsack problem with uncertain profits | In the case of a stochastic optimization such as this, you really should have an objective function that weights risk. Ideally, this would be a utility function, which can be converted to an expected utility when there's a probability distribution on reward and used instead. (Note this assumes that the utilities of i... | Knapsack problem with uncertain profits | In the case of a stochastic optimization such as this, you really should have an objective function that weights risk. Ideally, this would be a utility function, which can be converted to an expected | Knapsack problem with uncertain profits
In the case of a stochastic optimization such as this, you really should have an objective function that weights risk. Ideally, this would be a utility function, which can be converted to an expected utility when there's a probability distribution on reward and used instead. (N... | Knapsack problem with uncertain profits
In the case of a stochastic optimization such as this, you really should have an objective function that weights risk. Ideally, this would be a utility function, which can be converted to an expected |
43,213 | Please help me refine this zero-inflated negative binomial model | The zero inflated model is designed to deal with overdispersed data - 87% zeros is usually considered overdispersed, but you can check if mean < variance after fitting a Poisson. A good way to see if you have dealt with overdispersion is to simply predict the share of $0,1,2,\ldots$ in your sample with your model. If y... | Please help me refine this zero-inflated negative binomial model | The zero inflated model is designed to deal with overdispersed data - 87% zeros is usually considered overdispersed, but you can check if mean < variance after fitting a Poisson. A good way to see if | Please help me refine this zero-inflated negative binomial model
The zero inflated model is designed to deal with overdispersed data - 87% zeros is usually considered overdispersed, but you can check if mean < variance after fitting a Poisson. A good way to see if you have dealt with overdispersion is to simply predict... | Please help me refine this zero-inflated negative binomial model
The zero inflated model is designed to deal with overdispersed data - 87% zeros is usually considered overdispersed, but you can check if mean < variance after fitting a Poisson. A good way to see if |
43,214 | Algorithm for determining performance speedup/slowdown in a code change vs. historical data? | Here's something that's not really an answer to your question, but may be helpful for your problem:
One of the difficulties you mention is that you are doing ~400 t-tests, and so will end up with lots of spurious small p-values. One useful thing to use here is `false discovery rate' (FDR) analysis, which tries to deter... | Algorithm for determining performance speedup/slowdown in a code change vs. historical data? | Here's something that's not really an answer to your question, but may be helpful for your problem:
One of the difficulties you mention is that you are doing ~400 t-tests, and so will end up with lots | Algorithm for determining performance speedup/slowdown in a code change vs. historical data?
Here's something that's not really an answer to your question, but may be helpful for your problem:
One of the difficulties you mention is that you are doing ~400 t-tests, and so will end up with lots of spurious small p-values... | Algorithm for determining performance speedup/slowdown in a code change vs. historical data?
Here's something that's not really an answer to your question, but may be helpful for your problem:
One of the difficulties you mention is that you are doing ~400 t-tests, and so will end up with lots |
43,215 | Algorithm for determining performance speedup/slowdown in a code change vs. historical data? | HEre is something that isn't exactly an answer to the original question, but might be valuable and might also act as an answer to the question behind the question which is something to the effect of: "how do I make the most of my programming to speed up my code?"
I bet you can modify a section, and re-run several times... | Algorithm for determining performance speedup/slowdown in a code change vs. historical data? | HEre is something that isn't exactly an answer to the original question, but might be valuable and might also act as an answer to the question behind the question which is something to the effect of: | Algorithm for determining performance speedup/slowdown in a code change vs. historical data?
HEre is something that isn't exactly an answer to the original question, but might be valuable and might also act as an answer to the question behind the question which is something to the effect of: "how do I make the most of ... | Algorithm for determining performance speedup/slowdown in a code change vs. historical data?
HEre is something that isn't exactly an answer to the original question, but might be valuable and might also act as an answer to the question behind the question which is something to the effect of: |
43,216 | Algorithm for determining performance speedup/slowdown in a code change vs. historical data? | First you want to know if there has been a statistically significant change in total testing time. Second, if there has been a change, which tests have changed?
This is what I would do: Within each code state compute the mean for each time variable. Then for each time variable compute the standard deviation of its m... | Algorithm for determining performance speedup/slowdown in a code change vs. historical data? | First you want to know if there has been a statistically significant change in total testing time. Second, if there has been a change, which tests have changed?
This is what I would do: Within each | Algorithm for determining performance speedup/slowdown in a code change vs. historical data?
First you want to know if there has been a statistically significant change in total testing time. Second, if there has been a change, which tests have changed?
This is what I would do: Within each code state compute the mean... | Algorithm for determining performance speedup/slowdown in a code change vs. historical data?
First you want to know if there has been a statistically significant change in total testing time. Second, if there has been a change, which tests have changed?
This is what I would do: Within each |
43,217 | Co-occurrence of properties in a population | For a much smaller number of properties, consider a log-linear model, or perhaps some other generalized linear model depending on the underlying process generating your data. Specifically, each of the "properties" of interest should be considered a binary variable (presence vs absence of property). Note that this appro... | Co-occurrence of properties in a population | For a much smaller number of properties, consider a log-linear model, or perhaps some other generalized linear model depending on the underlying process generating your data. Specifically, each of the | Co-occurrence of properties in a population
For a much smaller number of properties, consider a log-linear model, or perhaps some other generalized linear model depending on the underlying process generating your data. Specifically, each of the "properties" of interest should be considered a binary variable (presence v... | Co-occurrence of properties in a population
For a much smaller number of properties, consider a log-linear model, or perhaps some other generalized linear model depending on the underlying process generating your data. Specifically, each of the |
43,218 | How to test whether mean and variance is the same in two small samples? | I would argue that it isn't possible to properly perform a joint test on the first two moments without knowing more about the distribution. Since there is no general rule as to how moments interact, it is impossible to construct a tight confidence region.
If you dare to make a normality assumption on both samples, the... | How to test whether mean and variance is the same in two small samples? | I would argue that it isn't possible to properly perform a joint test on the first two moments without knowing more about the distribution. Since there is no general rule as to how moments interact, i | How to test whether mean and variance is the same in two small samples?
I would argue that it isn't possible to properly perform a joint test on the first two moments without knowing more about the distribution. Since there is no general rule as to how moments interact, it is impossible to construct a tight confidence ... | How to test whether mean and variance is the same in two small samples?
I would argue that it isn't possible to properly perform a joint test on the first two moments without knowing more about the distribution. Since there is no general rule as to how moments interact, i |
43,219 | Forecasting optimization techniques in fantasy baseball | Accounting for variance
There's a lot to think about in optimising a lineup for fantasy sports. You're right that expectation and variance are huge parts of it. Naively it would seem that expected points earned is all that matters. However, certain contests will reward only the first place player out of thousands -- me... | Forecasting optimization techniques in fantasy baseball | Accounting for variance
There's a lot to think about in optimising a lineup for fantasy sports. You're right that expectation and variance are huge parts of it. Naively it would seem that expected poi | Forecasting optimization techniques in fantasy baseball
Accounting for variance
There's a lot to think about in optimising a lineup for fantasy sports. You're right that expectation and variance are huge parts of it. Naively it would seem that expected points earned is all that matters. However, certain contests will r... | Forecasting optimization techniques in fantasy baseball
Accounting for variance
There's a lot to think about in optimising a lineup for fantasy sports. You're right that expectation and variance are huge parts of it. Naively it would seem that expected poi |
43,220 | Forecasting optimization techniques in fantasy baseball | Running with your example of picking between two players given the knowledge of an opposing pitcher, I think you could build a reasonable model using historical data to simulate outcomes. For example, suppose you are deciding to whether to start Player A or Player B. Player A is a 31 year old RH batter facing a RH star... | Forecasting optimization techniques in fantasy baseball | Running with your example of picking between two players given the knowledge of an opposing pitcher, I think you could build a reasonable model using historical data to simulate outcomes. For example, | Forecasting optimization techniques in fantasy baseball
Running with your example of picking between two players given the knowledge of an opposing pitcher, I think you could build a reasonable model using historical data to simulate outcomes. For example, suppose you are deciding to whether to start Player A or Player... | Forecasting optimization techniques in fantasy baseball
Running with your example of picking between two players given the knowledge of an opposing pitcher, I think you could build a reasonable model using historical data to simulate outcomes. For example, |
43,221 | How to find +/- uncertainty with a least squares regression | You could treat it like a multiple imputation problem. Basically you just specify distributions to characterize your uncertainty for each point, then you take several draws of your dataset. Fit your model to each set of draws. You then average the coefficients, average the variance-covariance matrices, and add a non... | How to find +/- uncertainty with a least squares regression | You could treat it like a multiple imputation problem. Basically you just specify distributions to characterize your uncertainty for each point, then you take several draws of your dataset. Fit your | How to find +/- uncertainty with a least squares regression
You could treat it like a multiple imputation problem. Basically you just specify distributions to characterize your uncertainty for each point, then you take several draws of your dataset. Fit your model to each set of draws. You then average the coefficie... | How to find +/- uncertainty with a least squares regression
You could treat it like a multiple imputation problem. Basically you just specify distributions to characterize your uncertainty for each point, then you take several draws of your dataset. Fit your |
43,222 | How to find +/- uncertainty with a least squares regression | Consider a linear estimator $\mathbf{\hat{y}} = \mathbf{X\theta}$ fitted with linear regression $\mathbf{\theta} = (\mathbf{X^\top X})^{-1}\mathbf{X}^\top \mathbf{y}$.
If $\mathbf{C}_y = \mathrm{diag}(\sigma_1^2,\sigma_2^2,\ldots,\sigma_m^2)$ is the covariance for the observations $\mathbf{y}$, the covariance for $\mat... | How to find +/- uncertainty with a least squares regression | Consider a linear estimator $\mathbf{\hat{y}} = \mathbf{X\theta}$ fitted with linear regression $\mathbf{\theta} = (\mathbf{X^\top X})^{-1}\mathbf{X}^\top \mathbf{y}$.
If $\mathbf{C}_y = \mathrm{diag} | How to find +/- uncertainty with a least squares regression
Consider a linear estimator $\mathbf{\hat{y}} = \mathbf{X\theta}$ fitted with linear regression $\mathbf{\theta} = (\mathbf{X^\top X})^{-1}\mathbf{X}^\top \mathbf{y}$.
If $\mathbf{C}_y = \mathrm{diag}(\sigma_1^2,\sigma_2^2,\ldots,\sigma_m^2)$ is the covariance... | How to find +/- uncertainty with a least squares regression
Consider a linear estimator $\mathbf{\hat{y}} = \mathbf{X\theta}$ fitted with linear regression $\mathbf{\theta} = (\mathbf{X^\top X})^{-1}\mathbf{X}^\top \mathbf{y}$.
If $\mathbf{C}_y = \mathrm{diag} |
43,223 | Exploratory factor analysis using pooled longitudinal data | EFA is not the main issue here, you need to think long and hard about the meaning of correlations between your variables/questions.
Your willingness to assume independence is doing all the work and feels like a way to sidestep a difficult problem through wishful thinking. I don't see how it can be reasonable, even with... | Exploratory factor analysis using pooled longitudinal data | EFA is not the main issue here, you need to think long and hard about the meaning of correlations between your variables/questions.
Your willingness to assume independence is doing all the work and fe | Exploratory factor analysis using pooled longitudinal data
EFA is not the main issue here, you need to think long and hard about the meaning of correlations between your variables/questions.
Your willingness to assume independence is doing all the work and feels like a way to sidestep a difficult problem through wishfu... | Exploratory factor analysis using pooled longitudinal data
EFA is not the main issue here, you need to think long and hard about the meaning of correlations between your variables/questions.
Your willingness to assume independence is doing all the work and fe |
43,224 | Exploratory factor analysis using pooled longitudinal data | Putting the three time points as three times as many cases does not require assuming independence. Identically replicating three times the measurements obtained at a single time point would essentially produce the same factors as the factor analysis on that single time point. Therefore independence is not a problem. Pr... | Exploratory factor analysis using pooled longitudinal data | Putting the three time points as three times as many cases does not require assuming independence. Identically replicating three times the measurements obtained at a single time point would essentiall | Exploratory factor analysis using pooled longitudinal data
Putting the three time points as three times as many cases does not require assuming independence. Identically replicating three times the measurements obtained at a single time point would essentially produce the same factors as the factor analysis on that sin... | Exploratory factor analysis using pooled longitudinal data
Putting the three time points as three times as many cases does not require assuming independence. Identically replicating three times the measurements obtained at a single time point would essentiall |
43,225 | Hierarchical regression using residuals | After a bit more digging, I suppose I'll take a stab at answering this myself. Each of the last two solutions I proposed is one half of a partial regression plot (aka added variable plot). That is, to construct a partial regression plot, the residuals from regressing the response variable against all predictor variable... | Hierarchical regression using residuals | After a bit more digging, I suppose I'll take a stab at answering this myself. Each of the last two solutions I proposed is one half of a partial regression plot (aka added variable plot). That is, to | Hierarchical regression using residuals
After a bit more digging, I suppose I'll take a stab at answering this myself. Each of the last two solutions I proposed is one half of a partial regression plot (aka added variable plot). That is, to construct a partial regression plot, the residuals from regressing the response... | Hierarchical regression using residuals
After a bit more digging, I suppose I'll take a stab at answering this myself. Each of the last two solutions I proposed is one half of a partial regression plot (aka added variable plot). That is, to |
43,226 | How to find MLE when samples depend on the estimated parameter | I suggest you draw the likelihood as a function of $\theta$, without forgetting that $1/\theta$ must be greater than any observation (i.e. what are the bounds on $\theta$?).
Keep in mind that everything but $\theta^{2n}$ in the likelihood is a constant, and so you can write it as $c.\theta^{2n}$; so just draw $\cal{L}... | How to find MLE when samples depend on the estimated parameter | I suggest you draw the likelihood as a function of $\theta$, without forgetting that $1/\theta$ must be greater than any observation (i.e. what are the bounds on $\theta$?).
Keep in mind that everyth | How to find MLE when samples depend on the estimated parameter
I suggest you draw the likelihood as a function of $\theta$, without forgetting that $1/\theta$ must be greater than any observation (i.e. what are the bounds on $\theta$?).
Keep in mind that everything but $\theta^{2n}$ in the likelihood is a constant, an... | How to find MLE when samples depend on the estimated parameter
I suggest you draw the likelihood as a function of $\theta$, without forgetting that $1/\theta$ must be greater than any observation (i.e. what are the bounds on $\theta$?).
Keep in mind that everyth |
43,227 | How to find MLE when samples depend on the estimated parameter | In this kind of problem, it helps a lot to write explicitly the indicators in the definitions of the densities. You have $Y_1,\dots,Y_n$, conditionally IID given $\Theta=\theta$, such that
$$
f_{Y_i\mid\Theta}(y_i\mid\theta) = 2y_i\theta^2\, I_{[0,\,1/\theta]}(y_i) \, .
$$
Since $0\leq y_i\leq 1/\theta$ if and only i... | How to find MLE when samples depend on the estimated parameter | In this kind of problem, it helps a lot to write explicitly the indicators in the definitions of the densities. You have $Y_1,\dots,Y_n$, conditionally IID given $\Theta=\theta$, such that
$$
f_{Y_i | How to find MLE when samples depend on the estimated parameter
In this kind of problem, it helps a lot to write explicitly the indicators in the definitions of the densities. You have $Y_1,\dots,Y_n$, conditionally IID given $\Theta=\theta$, such that
$$
f_{Y_i\mid\Theta}(y_i\mid\theta) = 2y_i\theta^2\, I_{[0,\,1/\th... | How to find MLE when samples depend on the estimated parameter
In this kind of problem, it helps a lot to write explicitly the indicators in the definitions of the densities. You have $Y_1,\dots,Y_n$, conditionally IID given $\Theta=\theta$, such that
$$
f_{Y_i |
43,228 | Are there models for "censored" spatial point processes? | To start, this change in support problem is an active area of research, and so although it is usual to treat units of analysis in criminology as discrete, you can certainly make a case for treating it as a continuous field. Although I don't hold as such a negative view of using discrete units as you, I look forward to ... | Are there models for "censored" spatial point processes? | To start, this change in support problem is an active area of research, and so although it is usual to treat units of analysis in criminology as discrete, you can certainly make a case for treating it | Are there models for "censored" spatial point processes?
To start, this change in support problem is an active area of research, and so although it is usual to treat units of analysis in criminology as discrete, you can certainly make a case for treating it as a continuous field. Although I don't hold as such a negativ... | Are there models for "censored" spatial point processes?
To start, this change in support problem is an active area of research, and so although it is usual to treat units of analysis in criminology as discrete, you can certainly make a case for treating it |
43,229 | Are there models for "censored" spatial point processes? | Censored spatial data is very common in the field of Cosmology. The issue is typically dealt with by creating a sample of random points with a flat distribution but that receives the same censoring as the data you are trying to model. Then, your analysis is adjusted by comparing effects seen in your data relative to ef... | Are there models for "censored" spatial point processes? | Censored spatial data is very common in the field of Cosmology. The issue is typically dealt with by creating a sample of random points with a flat distribution but that receives the same censoring as | Are there models for "censored" spatial point processes?
Censored spatial data is very common in the field of Cosmology. The issue is typically dealt with by creating a sample of random points with a flat distribution but that receives the same censoring as the data you are trying to model. Then, your analysis is adjus... | Are there models for "censored" spatial point processes?
Censored spatial data is very common in the field of Cosmology. The issue is typically dealt with by creating a sample of random points with a flat distribution but that receives the same censoring as |
43,230 | How to create a random variables in a simulation using skewness and kurtosis as well as average and standard deviation input? | These features can be included in simulations from a symmetric distribution using transformations that control skewness and kurtosis such as the Johnson-SU transformation, the g-and-h, the g-and-k, the sinh-arcsinh, and the LambertW tranformations (Tukey-type transformations). A quick google search gives you relevant r... | How to create a random variables in a simulation using skewness and kurtosis as well as average and | These features can be included in simulations from a symmetric distribution using transformations that control skewness and kurtosis such as the Johnson-SU transformation, the g-and-h, the g-and-k, th | How to create a random variables in a simulation using skewness and kurtosis as well as average and standard deviation input?
These features can be included in simulations from a symmetric distribution using transformations that control skewness and kurtosis such as the Johnson-SU transformation, the g-and-h, the g-and... | How to create a random variables in a simulation using skewness and kurtosis as well as average and
These features can be included in simulations from a symmetric distribution using transformations that control skewness and kurtosis such as the Johnson-SU transformation, the g-and-h, the g-and-k, th |
43,231 | Bayesian and frequentist approaches: What are some success stories for the former? [duplicate] | Adrian Raftery examined a set of statistics about coal-dust explosions in 19th-century British mines. Frequentist techniques had shown the coal mining accident rates changed over time gradually. Our of curiosity, Raftery experimented with Bayes' Theorem, and discovered that accident rates had plummeted suddenly in the ... | Bayesian and frequentist approaches: What are some success stories for the former? [duplicate] | Adrian Raftery examined a set of statistics about coal-dust explosions in 19th-century British mines. Frequentist techniques had shown the coal mining accident rates changed over time gradually. Our o | Bayesian and frequentist approaches: What are some success stories for the former? [duplicate]
Adrian Raftery examined a set of statistics about coal-dust explosions in 19th-century British mines. Frequentist techniques had shown the coal mining accident rates changed over time gradually. Our of curiosity, Raftery expe... | Bayesian and frequentist approaches: What are some success stories for the former? [duplicate]
Adrian Raftery examined a set of statistics about coal-dust explosions in 19th-century British mines. Frequentist techniques had shown the coal mining accident rates changed over time gradually. Our o |
43,232 | Choosing between a MANOVA and a series of t-tests when comparing two groups | I have recently answered a very similar question, maybe you want to take a look: Assessing group differences on multiple outcomes. However, as the questions have not been marked as duplicates (and I am too new here to attempt it), let me add here the following.
You have a very simple design: only two groups; MANOVA is ... | Choosing between a MANOVA and a series of t-tests when comparing two groups | I have recently answered a very similar question, maybe you want to take a look: Assessing group differences on multiple outcomes. However, as the questions have not been marked as duplicates (and I a | Choosing between a MANOVA and a series of t-tests when comparing two groups
I have recently answered a very similar question, maybe you want to take a look: Assessing group differences on multiple outcomes. However, as the questions have not been marked as duplicates (and I am too new here to attempt it), let me add he... | Choosing between a MANOVA and a series of t-tests when comparing two groups
I have recently answered a very similar question, maybe you want to take a look: Assessing group differences on multiple outcomes. However, as the questions have not been marked as duplicates (and I a |
43,233 | Choosing between a MANOVA and a series of t-tests when comparing two groups | Many different statistical tests of significance can be applied in research studies. Factors such as the scale of measurement represented by the data, method of participant selection, number of groups being compared and number of independent variables determine which test of significance should be used in a given study... | Choosing between a MANOVA and a series of t-tests when comparing two groups | Many different statistical tests of significance can be applied in research studies. Factors such as the scale of measurement represented by the data, method of participant selection, number of groups | Choosing between a MANOVA and a series of t-tests when comparing two groups
Many different statistical tests of significance can be applied in research studies. Factors such as the scale of measurement represented by the data, method of participant selection, number of groups being compared and number of independent va... | Choosing between a MANOVA and a series of t-tests when comparing two groups
Many different statistical tests of significance can be applied in research studies. Factors such as the scale of measurement represented by the data, method of participant selection, number of groups |
43,234 | Obtaining a log-normal waiting time via sequential exponential or gamma distributions - is it possible? | As mentioned in my comment, a way to approximate the log-normal by a gamma distribution is through the use of the KL-divergence. That is, we choose the parameters of the gamma distribution to minimise
$$KL(\kappa,\theta)=\int_{0}^{\infty}p(z|\mu,\sigma)\log\left(\frac{p(z|\mu,\sigma)}{q(z|\kappa,\theta)}\right)dz$$
Wh... | Obtaining a log-normal waiting time via sequential exponential or gamma distributions - is it possib | As mentioned in my comment, a way to approximate the log-normal by a gamma distribution is through the use of the KL-divergence. That is, we choose the parameters of the gamma distribution to minimis | Obtaining a log-normal waiting time via sequential exponential or gamma distributions - is it possible?
As mentioned in my comment, a way to approximate the log-normal by a gamma distribution is through the use of the KL-divergence. That is, we choose the parameters of the gamma distribution to minimise
$$KL(\kappa,\t... | Obtaining a log-normal waiting time via sequential exponential or gamma distributions - is it possib
As mentioned in my comment, a way to approximate the log-normal by a gamma distribution is through the use of the KL-divergence. That is, we choose the parameters of the gamma distribution to minimis |
43,235 | Transforming two normal random variables | Because there's a subtlety here, this question is worth a correct answer. But let's develop it with as little work as possible, in the most straightforward manner.
What subtlety? The variables $(U,V)$ do not determine $(X,Y).$
The change of variables from $(X,Y)$ to $(U,V)$ is two-to-one: because $(U,V)$ gives us info... | Transforming two normal random variables | Because there's a subtlety here, this question is worth a correct answer. But let's develop it with as little work as possible, in the most straightforward manner.
What subtlety? The variables $(U,V) | Transforming two normal random variables
Because there's a subtlety here, this question is worth a correct answer. But let's develop it with as little work as possible, in the most straightforward manner.
What subtlety? The variables $(U,V)$ do not determine $(X,Y).$
The change of variables from $(X,Y)$ to $(U,V)$ is ... | Transforming two normal random variables
Because there's a subtlety here, this question is worth a correct answer. But let's develop it with as little work as possible, in the most straightforward manner.
What subtlety? The variables $(U,V) |
43,236 | Transforming two normal random variables | By Box Muller transformation
$X=r\cos(\theta) \hspace{.5cm}
Y=r\sin(\theta) \hspace{.5cm}
X,Y \sim normal(0,1) \Leftrightarrow \theta \sim Uniform(0,2\pi) \hspace{.5cm} r^2\sim chi(2)$.
$X$ and $Y$ are independent $\Leftrightarrow $ $\theta$ and $r$ are independent.
also $\sin(\theta) \sim \cos(\theta) \sim \sin(2\... | Transforming two normal random variables | By Box Muller transformation
$X=r\cos(\theta) \hspace{.5cm}
Y=r\sin(\theta) \hspace{.5cm}
X,Y \sim normal(0,1) \Leftrightarrow \theta \sim Uniform(0,2\pi) \hspace{.5cm} r^2\sim chi(2)$.
$X$ and $Y$ | Transforming two normal random variables
By Box Muller transformation
$X=r\cos(\theta) \hspace{.5cm}
Y=r\sin(\theta) \hspace{.5cm}
X,Y \sim normal(0,1) \Leftrightarrow \theta \sim Uniform(0,2\pi) \hspace{.5cm} r^2\sim chi(2)$.
$X$ and $Y$ are independent $\Leftrightarrow $ $\theta$ and $r$ are independent.
also $\si... | Transforming two normal random variables
By Box Muller transformation
$X=r\cos(\theta) \hspace{.5cm}
Y=r\sin(\theta) \hspace{.5cm}
X,Y \sim normal(0,1) \Leftrightarrow \theta \sim Uniform(0,2\pi) \hspace{.5cm} r^2\sim chi(2)$.
$X$ and $Y$ |
43,237 | Elementary MCMC pseudocode | If you're using Metropolis Hastings, then you don't have to worry too much about conjugacy of priors or finely-tuned proposal jumps.
Conjugate priors are useful for avoiding or short-circuiting numerical computation. They shouldn't be necessary for running Metropolis Hastings on a simple model.
For the proposal distri... | Elementary MCMC pseudocode | If you're using Metropolis Hastings, then you don't have to worry too much about conjugacy of priors or finely-tuned proposal jumps.
Conjugate priors are useful for avoiding or short-circuiting numer | Elementary MCMC pseudocode
If you're using Metropolis Hastings, then you don't have to worry too much about conjugacy of priors or finely-tuned proposal jumps.
Conjugate priors are useful for avoiding or short-circuiting numerical computation. They shouldn't be necessary for running Metropolis Hastings on a simple mod... | Elementary MCMC pseudocode
If you're using Metropolis Hastings, then you don't have to worry too much about conjugacy of priors or finely-tuned proposal jumps.
Conjugate priors are useful for avoiding or short-circuiting numer |
43,238 | How to test for mediation when working with binary data? | You can easily model this in structural modeling software such as Mplus. You need a model of
X --> Z --> Y
where Z is the mediator and inspect fit and/or residual correlations. If the model fit is poor, then Z may be an imperfect mediator, and residual correlations between the three variables should be inspected to see... | How to test for mediation when working with binary data? | You can easily model this in structural modeling software such as Mplus. You need a model of
X --> Z --> Y
where Z is the mediator and inspect fit and/or residual correlations. If the model fit is poo | How to test for mediation when working with binary data?
You can easily model this in structural modeling software such as Mplus. You need a model of
X --> Z --> Y
where Z is the mediator and inspect fit and/or residual correlations. If the model fit is poor, then Z may be an imperfect mediator, and residual correlatio... | How to test for mediation when working with binary data?
You can easily model this in structural modeling software such as Mplus. You need a model of
X --> Z --> Y
where Z is the mediator and inspect fit and/or residual correlations. If the model fit is poo |
43,239 | How to test for mediation when working with binary data? | Nathaniel Herr discussed the problem (and solution) of using Logit regression with dichotomous mediator, predictor and outcome in his blog
http://www.nrhpsych.com/mediation/logmed.html | How to test for mediation when working with binary data? | Nathaniel Herr discussed the problem (and solution) of using Logit regression with dichotomous mediator, predictor and outcome in his blog
http://www.nrhpsych.com/mediation/logmed.html | How to test for mediation when working with binary data?
Nathaniel Herr discussed the problem (and solution) of using Logit regression with dichotomous mediator, predictor and outcome in his blog
http://www.nrhpsych.com/mediation/logmed.html | How to test for mediation when working with binary data?
Nathaniel Herr discussed the problem (and solution) of using Logit regression with dichotomous mediator, predictor and outcome in his blog
http://www.nrhpsych.com/mediation/logmed.html |
43,240 | Variational inference for nested Chinese restaurant process | David Blei has an implementation of hLDA on his website, though I'm not sure if it's variational or MCMC. It's the second one from the bottom in the software list. | Variational inference for nested Chinese restaurant process | David Blei has an implementation of hLDA on his website, though I'm not sure if it's variational or MCMC. It's the second one from the bottom in the software list. | Variational inference for nested Chinese restaurant process
David Blei has an implementation of hLDA on his website, though I'm not sure if it's variational or MCMC. It's the second one from the bottom in the software list. | Variational inference for nested Chinese restaurant process
David Blei has an implementation of hLDA on his website, though I'm not sure if it's variational or MCMC. It's the second one from the bottom in the software list. |
43,241 | Is there a practical limit to the size of a piece of data with 0 statistical redundancy? | I'll ty to answer the question in terms of Kolmogorov complexity, which is the length of the smallest description of a finite sequence, given a fixed description language. A sequence is called Kolmogorov random, if the Kolmogorov complexity is at least as big as the length of the sequence (i.e., the sequence is incompr... | Is there a practical limit to the size of a piece of data with 0 statistical redundancy? | I'll ty to answer the question in terms of Kolmogorov complexity, which is the length of the smallest description of a finite sequence, given a fixed description language. A sequence is called Kolmogo | Is there a practical limit to the size of a piece of data with 0 statistical redundancy?
I'll ty to answer the question in terms of Kolmogorov complexity, which is the length of the smallest description of a finite sequence, given a fixed description language. A sequence is called Kolmogorov random, if the Kolmogorov c... | Is there a practical limit to the size of a piece of data with 0 statistical redundancy?
I'll ty to answer the question in terms of Kolmogorov complexity, which is the length of the smallest description of a finite sequence, given a fixed description language. A sequence is called Kolmogo |
43,242 | Is there a practical limit to the size of a piece of data with 0 statistical redundancy? | I’ll improve shortly on my above comment: If a compression algorithm is fixed, an arbitrary large non-compressible (with this algorithm) piece of data can be obtained just by iterating the algorithm. If a cycle appear before hitting the desired size, pick some starting point out of this cycle, and try again.
$\def\N\{\... | Is there a practical limit to the size of a piece of data with 0 statistical redundancy? | I’ll improve shortly on my above comment: If a compression algorithm is fixed, an arbitrary large non-compressible (with this algorithm) piece of data can be obtained just by iterating the algorithm. | Is there a practical limit to the size of a piece of data with 0 statistical redundancy?
I’ll improve shortly on my above comment: If a compression algorithm is fixed, an arbitrary large non-compressible (with this algorithm) piece of data can be obtained just by iterating the algorithm. If a cycle appear before hittin... | Is there a practical limit to the size of a piece of data with 0 statistical redundancy?
I’ll improve shortly on my above comment: If a compression algorithm is fixed, an arbitrary large non-compressible (with this algorithm) piece of data can be obtained just by iterating the algorithm. |
43,243 | Probability that at least one person at a party will accidentally choose their own gift | Why not build a stochastic simulation? You can get an empirical estimate.
number the gifts according to who brought them. Gift 1 was brought by person 1.
Uniformly randomly draw gifts without replacement
count how many gifts were drawn at their number. So if gift 10 was the tenth one drawn, count it.
store the value... | Probability that at least one person at a party will accidentally choose their own gift | Why not build a stochastic simulation? You can get an empirical estimate.
number the gifts according to who brought them. Gift 1 was brought by person 1.
Uniformly randomly draw gifts without replac | Probability that at least one person at a party will accidentally choose their own gift
Why not build a stochastic simulation? You can get an empirical estimate.
number the gifts according to who brought them. Gift 1 was brought by person 1.
Uniformly randomly draw gifts without replacement
count how many gifts were ... | Probability that at least one person at a party will accidentally choose their own gift
Why not build a stochastic simulation? You can get an empirical estimate.
number the gifts according to who brought them. Gift 1 was brought by person 1.
Uniformly randomly draw gifts without replac |
43,244 | What level to use when making inferences on the group mean in a hierarchical Bayesian analysis? | What has happened is that the estimates of the individual subject means have been shrunk towards the group mean, causing the std. deviation of the subject means (and consequently that of the mean of the subject means) to be "too small". This shrinkage is part and parcel of the hierarchical Bayesian approach. The grou... | What level to use when making inferences on the group mean in a hierarchical Bayesian analysis? | What has happened is that the estimates of the individual subject means have been shrunk towards the group mean, causing the std. deviation of the subject means (and consequently that of the mean of t | What level to use when making inferences on the group mean in a hierarchical Bayesian analysis?
What has happened is that the estimates of the individual subject means have been shrunk towards the group mean, causing the std. deviation of the subject means (and consequently that of the mean of the subject means) to be ... | What level to use when making inferences on the group mean in a hierarchical Bayesian analysis?
What has happened is that the estimates of the individual subject means have been shrunk towards the group mean, causing the std. deviation of the subject means (and consequently that of the mean of t |
43,245 | What level to use when making inferences on the group mean in a hierarchical Bayesian analysis? | If you wish to make inferences about the group level then your distribution of interest is $p(\mu_{gr}|Y)$. The simple reason is that $\mu_{gr}$ is actually a random variable in your generative model. Mean_mu is not a random variable in your generative model, it is a statistic that either your sampler happened to gener... | What level to use when making inferences on the group mean in a hierarchical Bayesian analysis? | If you wish to make inferences about the group level then your distribution of interest is $p(\mu_{gr}|Y)$. The simple reason is that $\mu_{gr}$ is actually a random variable in your generative model. | What level to use when making inferences on the group mean in a hierarchical Bayesian analysis?
If you wish to make inferences about the group level then your distribution of interest is $p(\mu_{gr}|Y)$. The simple reason is that $\mu_{gr}$ is actually a random variable in your generative model. Mean_mu is not a random... | What level to use when making inferences on the group mean in a hierarchical Bayesian analysis?
If you wish to make inferences about the group level then your distribution of interest is $p(\mu_{gr}|Y)$. The simple reason is that $\mu_{gr}$ is actually a random variable in your generative model. |
43,246 | Study replication from a Bayesian point of view | Looks like you're talking about Meta-Analysis, a statistical study on previous studies.
This is not a exclusively Bayesian concept, there are many frequentist meta-analysis, but as this chapter points out, it is a good fit for Bayesian statistics.
A google search of 'bayesian meta-analysis' turns up many articles. | Study replication from a Bayesian point of view | Looks like you're talking about Meta-Analysis, a statistical study on previous studies.
This is not a exclusively Bayesian concept, there are many frequentist meta-analysis, but as this chapter points | Study replication from a Bayesian point of view
Looks like you're talking about Meta-Analysis, a statistical study on previous studies.
This is not a exclusively Bayesian concept, there are many frequentist meta-analysis, but as this chapter points out, it is a good fit for Bayesian statistics.
A google search of 'bay... | Study replication from a Bayesian point of view
Looks like you're talking about Meta-Analysis, a statistical study on previous studies.
This is not a exclusively Bayesian concept, there are many frequentist meta-analysis, but as this chapter points |
43,247 | Study replication from a Bayesian point of view | The "direct replication" you mention is an issue; whatever parameter is being assessed in Study A is also there in Study B, so it makes no sense to say an effect is e.g. positive in one and negative in the other, or zero in one and not the other, or whatever - meaning non-replication can not happen in Bayesian analyses... | Study replication from a Bayesian point of view | The "direct replication" you mention is an issue; whatever parameter is being assessed in Study A is also there in Study B, so it makes no sense to say an effect is e.g. positive in one and negative i | Study replication from a Bayesian point of view
The "direct replication" you mention is an issue; whatever parameter is being assessed in Study A is also there in Study B, so it makes no sense to say an effect is e.g. positive in one and negative in the other, or zero in one and not the other, or whatever - meaning non... | Study replication from a Bayesian point of view
The "direct replication" you mention is an issue; whatever parameter is being assessed in Study A is also there in Study B, so it makes no sense to say an effect is e.g. positive in one and negative i |
43,248 | Study replication from a Bayesian point of view | My first reaction is what's the confidence intervals on those estimates (or credible interval if it was a Bayesian analysis)?
If the confidence interval is something like,
95% CI [ .1, .5] and 95% CI [.2, .14] I'd conclude that they estimate d to be within a similar range. Even if the results of one of the studies was... | Study replication from a Bayesian point of view | My first reaction is what's the confidence intervals on those estimates (or credible interval if it was a Bayesian analysis)?
If the confidence interval is something like,
95% CI [ .1, .5] and 95% CI | Study replication from a Bayesian point of view
My first reaction is what's the confidence intervals on those estimates (or credible interval if it was a Bayesian analysis)?
If the confidence interval is something like,
95% CI [ .1, .5] and 95% CI [.2, .14] I'd conclude that they estimate d to be within a similar rang... | Study replication from a Bayesian point of view
My first reaction is what's the confidence intervals on those estimates (or credible interval if it was a Bayesian analysis)?
If the confidence interval is something like,
95% CI [ .1, .5] and 95% CI |
43,249 | Testing for Spatial Autocorrelation in a Negative Binomial Regression Model | I suspect you can use moran.mc to do a monte-carlo permutation test. Basically, it computes a measure of spatial correlation for your residuals, then randomly reassigns your residuals to your regions and recomputes the measure. Do that 999 times, see where your data measure ranks with your MC measures. If its in the fa... | Testing for Spatial Autocorrelation in a Negative Binomial Regression Model | I suspect you can use moran.mc to do a monte-carlo permutation test. Basically, it computes a measure of spatial correlation for your residuals, then randomly reassigns your residuals to your regions | Testing for Spatial Autocorrelation in a Negative Binomial Regression Model
I suspect you can use moran.mc to do a monte-carlo permutation test. Basically, it computes a measure of spatial correlation for your residuals, then randomly reassigns your residuals to your regions and recomputes the measure. Do that 999 time... | Testing for Spatial Autocorrelation in a Negative Binomial Regression Model
I suspect you can use moran.mc to do a monte-carlo permutation test. Basically, it computes a measure of spatial correlation for your residuals, then randomly reassigns your residuals to your regions |
43,250 | Showing that the power of a test approaches 1 as the sample size approaches infinity | You are almost there, but you need to make your arguments more formal.
Rewrite the null and alternative hypothesis more generally so that they read
$$
\begin{align}
\mathfrak{h}_0{}:{}\beta_1 &= \beta^0_1\\
\mathfrak{h}_a{}:{}\beta_1&=\beta_1^a
\end{align}
$$
Then, using standard machinery, and under the linear regr... | Showing that the power of a test approaches 1 as the sample size approaches infinity | You are almost there, but you need to make your arguments more formal.
Rewrite the null and alternative hypothesis more generally so that they read
$$
\begin{align}
\mathfrak{h}_0{}:{}\beta_1 &= \be | Showing that the power of a test approaches 1 as the sample size approaches infinity
You are almost there, but you need to make your arguments more formal.
Rewrite the null and alternative hypothesis more generally so that they read
$$
\begin{align}
\mathfrak{h}_0{}:{}\beta_1 &= \beta^0_1\\
\mathfrak{h}_a{}:{}\beta_1... | Showing that the power of a test approaches 1 as the sample size approaches infinity
You are almost there, but you need to make your arguments more formal.
Rewrite the null and alternative hypothesis more generally so that they read
$$
\begin{align}
\mathfrak{h}_0{}:{}\beta_1 &= \be |
43,251 | How to interpret coefficients in a regression with ARIMA errors? | To answer some of my own questions, after additional reading and experimenting:
The trick with the coefficient is that it's in the space of the data after Box-Cox transformation. So invert the transformation to get the beta in the original units.
Yes, the standard approach in intervention analysis is to use a step fu... | How to interpret coefficients in a regression with ARIMA errors? | To answer some of my own questions, after additional reading and experimenting:
The trick with the coefficient is that it's in the space of the data after Box-Cox transformation. So invert the transf | How to interpret coefficients in a regression with ARIMA errors?
To answer some of my own questions, after additional reading and experimenting:
The trick with the coefficient is that it's in the space of the data after Box-Cox transformation. So invert the transformation to get the beta in the original units.
Yes, t... | How to interpret coefficients in a regression with ARIMA errors?
To answer some of my own questions, after additional reading and experimenting:
The trick with the coefficient is that it's in the space of the data after Box-Cox transformation. So invert the transf |
43,252 | First iteration in MCMC coda chain is different from initial values | I haven't dug into the JAGS source code, but often people consider the initial values to be iteration 0, and for iteration 1 to be the result after a single pass through the Gibbs sampler.
Also, if there are any Metropolis steps, there is likely to be a short adaptation phase before iteration 1 irrespective of the burn... | First iteration in MCMC coda chain is different from initial values | I haven't dug into the JAGS source code, but often people consider the initial values to be iteration 0, and for iteration 1 to be the result after a single pass through the Gibbs sampler.
Also, if th | First iteration in MCMC coda chain is different from initial values
I haven't dug into the JAGS source code, but often people consider the initial values to be iteration 0, and for iteration 1 to be the result after a single pass through the Gibbs sampler.
Also, if there are any Metropolis steps, there is likely to be ... | First iteration in MCMC coda chain is different from initial values
I haven't dug into the JAGS source code, but often people consider the initial values to be iteration 0, and for iteration 1 to be the result after a single pass through the Gibbs sampler.
Also, if th |
43,253 | First iteration in MCMC coda chain is different from initial values | First, JAGS doesn't do burn-in clipping - R2jags does something, but I don't know what that is. Second, adaptation (see the JAGS user manual) may affect initial values. Third, even if you turn off adaptation, R2jags used to have a bug with initial values. Better to use rjags instead. | First iteration in MCMC coda chain is different from initial values | First, JAGS doesn't do burn-in clipping - R2jags does something, but I don't know what that is. Second, adaptation (see the JAGS user manual) may affect initial values. Third, even if you turn off ada | First iteration in MCMC coda chain is different from initial values
First, JAGS doesn't do burn-in clipping - R2jags does something, but I don't know what that is. Second, adaptation (see the JAGS user manual) may affect initial values. Third, even if you turn off adaptation, R2jags used to have a bug with initial valu... | First iteration in MCMC coda chain is different from initial values
First, JAGS doesn't do burn-in clipping - R2jags does something, but I don't know what that is. Second, adaptation (see the JAGS user manual) may affect initial values. Third, even if you turn off ada |
43,254 | Comparing structural equation models with multivariate non-normality | This won't work, the models rarely get inflated in the same way. The proper analysis should involve Satorra-Benter scaled difference. I am not sure as to what the bootstrap analogues would be, although I am sure something can be constructed along the lines of the Bollen-Stine bootstrap (which should have become known a... | Comparing structural equation models with multivariate non-normality | This won't work, the models rarely get inflated in the same way. The proper analysis should involve Satorra-Benter scaled difference. I am not sure as to what the bootstrap analogues would be, althoug | Comparing structural equation models with multivariate non-normality
This won't work, the models rarely get inflated in the same way. The proper analysis should involve Satorra-Benter scaled difference. I am not sure as to what the bootstrap analogues would be, although I am sure something can be constructed along the ... | Comparing structural equation models with multivariate non-normality
This won't work, the models rarely get inflated in the same way. The proper analysis should involve Satorra-Benter scaled difference. I am not sure as to what the bootstrap analogues would be, althoug |
43,255 | On the applicability of Benjamini-Hochberg | Instead of independent tests you have dependent tests. But multiple testing concepts like familywise error rate (FWER) and false discovery rate (FDR) still apply. The complication is in the computation of quantities like FWER as probabilities simultaneously rejecting two of the hypotheses is no longer the product of ... | On the applicability of Benjamini-Hochberg | Instead of independent tests you have dependent tests. But multiple testing concepts like familywise error rate (FWER) and false discovery rate (FDR) still apply. The complication is in the computat | On the applicability of Benjamini-Hochberg
Instead of independent tests you have dependent tests. But multiple testing concepts like familywise error rate (FWER) and false discovery rate (FDR) still apply. The complication is in the computation of quantities like FWER as probabilities simultaneously rejecting two of ... | On the applicability of Benjamini-Hochberg
Instead of independent tests you have dependent tests. But multiple testing concepts like familywise error rate (FWER) and false discovery rate (FDR) still apply. The complication is in the computat |
43,256 | On the applicability of Benjamini-Hochberg | Sorted test statistics are not a problem for B-H as the (re)sorting is an inherent part of the procedure.
In the eigenvalue case-- when testing for the rank of the eigenspace-- it is actually the dependence between the eigenvalues that is more problematic. I am not sure if the PRDS condition needed for B-H is satisfi... | On the applicability of Benjamini-Hochberg | Sorted test statistics are not a problem for B-H as the (re)sorting is an inherent part of the procedure.
In the eigenvalue case-- when testing for the rank of the eigenspace-- it is actually the de | On the applicability of Benjamini-Hochberg
Sorted test statistics are not a problem for B-H as the (re)sorting is an inherent part of the procedure.
In the eigenvalue case-- when testing for the rank of the eigenspace-- it is actually the dependence between the eigenvalues that is more problematic. I am not sure if t... | On the applicability of Benjamini-Hochberg
Sorted test statistics are not a problem for B-H as the (re)sorting is an inherent part of the procedure.
In the eigenvalue case-- when testing for the rank of the eigenspace-- it is actually the de |
43,257 | Linear regression vs analysis of variance: how to explain the difference? | ANOVA and Linear regression are twin princesses grown in different castles. Please see the book of Andy Field: Discovering statistics using SPSS. He has a very nice explanation including the evolution in time of this two. Anyway put bluntly: they are very similar and developed in parallel for a certain period of time b... | Linear regression vs analysis of variance: how to explain the difference? | ANOVA and Linear regression are twin princesses grown in different castles. Please see the book of Andy Field: Discovering statistics using SPSS. He has a very nice explanation including the evolution | Linear regression vs analysis of variance: how to explain the difference?
ANOVA and Linear regression are twin princesses grown in different castles. Please see the book of Andy Field: Discovering statistics using SPSS. He has a very nice explanation including the evolution in time of this two. Anyway put bluntly: they... | Linear regression vs analysis of variance: how to explain the difference?
ANOVA and Linear regression are twin princesses grown in different castles. Please see the book of Andy Field: Discovering statistics using SPSS. He has a very nice explanation including the evolution |
43,258 | Lag length selection Granger causality test | The question here is really about the best way to select lag length for a VAR, as I noted in this answer. Granger causality doesn't even enter into it until your model for the time series is selected, which is why you may not see many papers specifically concerned with lag order for Granger causality tests. It's more a... | Lag length selection Granger causality test | The question here is really about the best way to select lag length for a VAR, as I noted in this answer. Granger causality doesn't even enter into it until your model for the time series is selected, | Lag length selection Granger causality test
The question here is really about the best way to select lag length for a VAR, as I noted in this answer. Granger causality doesn't even enter into it until your model for the time series is selected, which is why you may not see many papers specifically concerned with lag or... | Lag length selection Granger causality test
The question here is really about the best way to select lag length for a VAR, as I noted in this answer. Granger causality doesn't even enter into it until your model for the time series is selected, |
43,259 | Double exponential smoothing in multivariate multilevel panel regression | Double exponential smoothing can viewed as reduced version of Kalman filter.
It is not optimal but can be more robust. You may try Kalman filtering in R. | Double exponential smoothing in multivariate multilevel panel regression | Double exponential smoothing can viewed as reduced version of Kalman filter.
It is not optimal but can be more robust. You may try Kalman filtering in R. | Double exponential smoothing in multivariate multilevel panel regression
Double exponential smoothing can viewed as reduced version of Kalman filter.
It is not optimal but can be more robust. You may try Kalman filtering in R. | Double exponential smoothing in multivariate multilevel panel regression
Double exponential smoothing can viewed as reduced version of Kalman filter.
It is not optimal but can be more robust. You may try Kalman filtering in R. |
43,260 | Pairwise Mahalanobis distance in R [duplicate] | The following worked for me in similar example where R is a dataframe of 54 individuals and 8 variables. Mahalanobis distance Ma between individuals X1 and X2 can be computed as ff:
# express difference (X1-X2) as atomic row vector
d <- as.matrix(X1-X2)[1,]
# solve (covariance matrix) %*% x = d for x
x <- solve(cov(... | Pairwise Mahalanobis distance in R [duplicate] | The following worked for me in similar example where R is a dataframe of 54 individuals and 8 variables. Mahalanobis distance Ma between individuals X1 and X2 can be computed as ff:
# express differen | Pairwise Mahalanobis distance in R [duplicate]
The following worked for me in similar example where R is a dataframe of 54 individuals and 8 variables. Mahalanobis distance Ma between individuals X1 and X2 can be computed as ff:
# express difference (X1-X2) as atomic row vector
d <- as.matrix(X1-X2)[1,]
# solve (cov... | Pairwise Mahalanobis distance in R [duplicate]
The following worked for me in similar example where R is a dataframe of 54 individuals and 8 variables. Mahalanobis distance Ma between individuals X1 and X2 can be computed as ff:
# express differen |
43,261 | Pairwise Mahalanobis distance in R [duplicate] | You could try the gendistance function in the nbpMatching package
Here's a short example modified from the help page, with two variables instead of 10:
df <- data.frame(id=1:33, val1=rnorm(33), val2=rnorm(33))
df.dist <- gendistance(df, idcol=1)
df.dist$dist
The distance matrix will have a 34th row/column-- this is fo... | Pairwise Mahalanobis distance in R [duplicate] | You could try the gendistance function in the nbpMatching package
Here's a short example modified from the help page, with two variables instead of 10:
df <- data.frame(id=1:33, val1=rnorm(33), val2=r | Pairwise Mahalanobis distance in R [duplicate]
You could try the gendistance function in the nbpMatching package
Here's a short example modified from the help page, with two variables instead of 10:
df <- data.frame(id=1:33, val1=rnorm(33), val2=rnorm(33))
df.dist <- gendistance(df, idcol=1)
df.dist$dist
The distance ... | Pairwise Mahalanobis distance in R [duplicate]
You could try the gendistance function in the nbpMatching package
Here's a short example modified from the help page, with two variables instead of 10:
df <- data.frame(id=1:33, val1=rnorm(33), val2=r |
43,262 | Pairwise Mahalanobis distance in R [duplicate] | There a very easy way to do it using R Package "biotools".
In this case you will get a Squared Distance Mahalanobis Matrix.
#Manly (2004, p.65-66)
x1 <- c(131.37, 132.37, 134.47, 135.50, 136.17)
x2 <- c(133.60, 132.70, 133.80, 132.30, 130.33)
x3 <- c(99.17, 99.07, 96.03, 94.53, 93.50)
x4 <- c(50.53, 50.23, 50.57, 51.9... | Pairwise Mahalanobis distance in R [duplicate] | There a very easy way to do it using R Package "biotools".
In this case you will get a Squared Distance Mahalanobis Matrix.
#Manly (2004, p.65-66)
x1 <- c(131.37, 132.37, 134.47, 135.50, 136.17)
x2 < | Pairwise Mahalanobis distance in R [duplicate]
There a very easy way to do it using R Package "biotools".
In this case you will get a Squared Distance Mahalanobis Matrix.
#Manly (2004, p.65-66)
x1 <- c(131.37, 132.37, 134.47, 135.50, 136.17)
x2 <- c(133.60, 132.70, 133.80, 132.30, 130.33)
x3 <- c(99.17, 99.07, 96.03, ... | Pairwise Mahalanobis distance in R [duplicate]
There a very easy way to do it using R Package "biotools".
In this case you will get a Squared Distance Mahalanobis Matrix.
#Manly (2004, p.65-66)
x1 <- c(131.37, 132.37, 134.47, 135.50, 136.17)
x2 < |
43,263 | Pairwise Mahalanobis distance in R [duplicate] | Here is the code to do it:
library("MASS")
library("ICSNP")
x0<-mvrnorm(33,1:10,diag(c(seq(1,1/2,l=10)),10))
x1<-pair.diff(x0) #C-implementation.
dM<-mahalanobis(x1,colMeans(x1),var(x1))
Following Roman Luštrik's suggestion, here are more details.
The OP asked for pairwise Mahalanobis distance, which are multivari... | Pairwise Mahalanobis distance in R [duplicate] | Here is the code to do it:
library("MASS")
library("ICSNP")
x0<-mvrnorm(33,1:10,diag(c(seq(1,1/2,l=10)),10))
x1<-pair.diff(x0) #C-implementation.
dM<-mahalanobis(x1,colMeans(x1),var(x1))
Following | Pairwise Mahalanobis distance in R [duplicate]
Here is the code to do it:
library("MASS")
library("ICSNP")
x0<-mvrnorm(33,1:10,diag(c(seq(1,1/2,l=10)),10))
x1<-pair.diff(x0) #C-implementation.
dM<-mahalanobis(x1,colMeans(x1),var(x1))
Following Roman Luštrik's suggestion, here are more details.
The OP asked for pai... | Pairwise Mahalanobis distance in R [duplicate]
Here is the code to do it:
library("MASS")
library("ICSNP")
x0<-mvrnorm(33,1:10,diag(c(seq(1,1/2,l=10)),10))
x1<-pair.diff(x0) #C-implementation.
dM<-mahalanobis(x1,colMeans(x1),var(x1))
Following |
43,264 | Repeated measurements with missing values | Let me propose an answer - I am happy to hear constructive criticism:
Merge measurements of technical replicates (taking the union)
I assume that the between-replicates variation is about the same as the within-replicate variation (for any variable).
Apply statistical tests of difference of mean for the combined dat... | Repeated measurements with missing values | Let me propose an answer - I am happy to hear constructive criticism:
Merge measurements of technical replicates (taking the union)
I assume that the between-replicates variation is about the same a | Repeated measurements with missing values
Let me propose an answer - I am happy to hear constructive criticism:
Merge measurements of technical replicates (taking the union)
I assume that the between-replicates variation is about the same as the within-replicate variation (for any variable).
Apply statistical tests ... | Repeated measurements with missing values
Let me propose an answer - I am happy to hear constructive criticism:
Merge measurements of technical replicates (taking the union)
I assume that the between-replicates variation is about the same a |
43,265 | Support vector machine for text classification | LibSVM hasn't been getting reliable performance for me, of late. Have you tried using SVMLight ever?
You might also try looking at which features are showing the most predictive power in your model, and adding some sort of enriched-type feature. For example, if I were classifying documents on whether they contain infor... | Support vector machine for text classification | LibSVM hasn't been getting reliable performance for me, of late. Have you tried using SVMLight ever?
You might also try looking at which features are showing the most predictive power in your model, a | Support vector machine for text classification
LibSVM hasn't been getting reliable performance for me, of late. Have you tried using SVMLight ever?
You might also try looking at which features are showing the most predictive power in your model, and adding some sort of enriched-type feature. For example, if I were clas... | Support vector machine for text classification
LibSVM hasn't been getting reliable performance for me, of late. Have you tried using SVMLight ever?
You might also try looking at which features are showing the most predictive power in your model, a |
43,266 | Support vector machine for text classification | If you haven't done so, you can usually gain efficiency by stemming the words, as I explain on this post.
Stemming will replace words by their stem, so that for instance 'sky' and 'skies', or 'hope', 'hopes' and 'hoped', will be recognized as identical. If your texts are in English, you will most likely end up using Po... | Support vector machine for text classification | If you haven't done so, you can usually gain efficiency by stemming the words, as I explain on this post.
Stemming will replace words by their stem, so that for instance 'sky' and 'skies', or 'hope', | Support vector machine for text classification
If you haven't done so, you can usually gain efficiency by stemming the words, as I explain on this post.
Stemming will replace words by their stem, so that for instance 'sky' and 'skies', or 'hope', 'hopes' and 'hoped', will be recognized as identical. If your texts are i... | Support vector machine for text classification
If you haven't done so, you can usually gain efficiency by stemming the words, as I explain on this post.
Stemming will replace words by their stem, so that for instance 'sky' and 'skies', or 'hope', |
43,267 | Support vector machine for text classification | A simple thing to try, if you haven't done so already, is to normalize each document vector so the magnitude is 1.
SVMs tend to perform better if the magnitude of each training vector is similar. | Support vector machine for text classification | A simple thing to try, if you haven't done so already, is to normalize each document vector so the magnitude is 1.
SVMs tend to perform better if the magnitude of each training vector is similar. | Support vector machine for text classification
A simple thing to try, if you haven't done so already, is to normalize each document vector so the magnitude is 1.
SVMs tend to perform better if the magnitude of each training vector is similar. | Support vector machine for text classification
A simple thing to try, if you haven't done so already, is to normalize each document vector so the magnitude is 1.
SVMs tend to perform better if the magnitude of each training vector is similar. |
43,268 | Support vector machine for text classification | You can clean up your tekst documents using Soundex, a phonetic algorithm for indexing words by sound. This Soundex methods reduces the number of spelling mistakes.
There is a good implementation of soundex in SAS, namely the function Soundex() | Support vector machine for text classification | You can clean up your tekst documents using Soundex, a phonetic algorithm for indexing words by sound. This Soundex methods reduces the number of spelling mistakes.
There is a good implementation of s | Support vector machine for text classification
You can clean up your tekst documents using Soundex, a phonetic algorithm for indexing words by sound. This Soundex methods reduces the number of spelling mistakes.
There is a good implementation of soundex in SAS, namely the function Soundex() | Support vector machine for text classification
You can clean up your tekst documents using Soundex, a phonetic algorithm for indexing words by sound. This Soundex methods reduces the number of spelling mistakes.
There is a good implementation of s |
43,269 | How to perform regression on panel-data with timelag in SPSS / PASW? | You may want to apply the Fama-MacBeth regression technique outlined in their 1972 paper (link is a PDF, couldn't find a regular citation page for it.) This is a crude method since it doesn't do much residual clustering or analysis, but it's easy to implement and almost certainly already exists in SPSS.
Instead of risk... | How to perform regression on panel-data with timelag in SPSS / PASW? | You may want to apply the Fama-MacBeth regression technique outlined in their 1972 paper (link is a PDF, couldn't find a regular citation page for it.) This is a crude method since it doesn't do much | How to perform regression on panel-data with timelag in SPSS / PASW?
You may want to apply the Fama-MacBeth regression technique outlined in their 1972 paper (link is a PDF, couldn't find a regular citation page for it.) This is a crude method since it doesn't do much residual clustering or analysis, but it's easy to i... | How to perform regression on panel-data with timelag in SPSS / PASW?
You may want to apply the Fama-MacBeth regression technique outlined in their 1972 paper (link is a PDF, couldn't find a regular citation page for it.) This is a crude method since it doesn't do much |
43,270 | How to perform regression on panel-data with timelag in SPSS / PASW? | For easy to running your data analysis, try the eviews software or stata, etc. In SPSS, if you want to running the time lag data analysis, you must perform the data, like 1 to 2 etc. For example:
x : 12, 13,12,17,9,12
You can create the new variable x1 : 13, 12, 17, 9 and 12. The first number data variable for x not ... | How to perform regression on panel-data with timelag in SPSS / PASW? | For easy to running your data analysis, try the eviews software or stata, etc. In SPSS, if you want to running the time lag data analysis, you must perform the data, like 1 to 2 etc. For example:
x : | How to perform regression on panel-data with timelag in SPSS / PASW?
For easy to running your data analysis, try the eviews software or stata, etc. In SPSS, if you want to running the time lag data analysis, you must perform the data, like 1 to 2 etc. For example:
x : 12, 13,12,17,9,12
You can create the new variable... | How to perform regression on panel-data with timelag in SPSS / PASW?
For easy to running your data analysis, try the eviews software or stata, etc. In SPSS, if you want to running the time lag data analysis, you must perform the data, like 1 to 2 etc. For example:
x : |
43,271 | Contraindication for STL decompostion | I think with LOESS like any other smoother results will depend on the degree of smoothing. So I think that you can get very different decompositions depending on the amount of smoothing. How much waviness is do to periodicity and how much is just random noise? i think this could be difficult to say. Similar problem ... | Contraindication for STL decompostion | I think with LOESS like any other smoother results will depend on the degree of smoothing. So I think that you can get very different decompositions depending on the amount of smoothing. How much wa | Contraindication for STL decompostion
I think with LOESS like any other smoother results will depend on the degree of smoothing. So I think that you can get very different decompositions depending on the amount of smoothing. How much waviness is do to periodicity and how much is just random noise? i think this could... | Contraindication for STL decompostion
I think with LOESS like any other smoother results will depend on the degree of smoothing. So I think that you can get very different decompositions depending on the amount of smoothing. How much wa |
43,272 | What's the simplest way to create a beanplot in MATLAB? [closed] | I found an excellent series of plot tools on Matlab File Exchange that creates bean plots among several other distribution plots.
http://www.mathworks.com/matlabcentral/fileexchange/23661-violin-plots-for-plotting-multiple-distributions-distributionplot-m | What's the simplest way to create a beanplot in MATLAB? [closed] | I found an excellent series of plot tools on Matlab File Exchange that creates bean plots among several other distribution plots.
http://www.mathworks.com/matlabcentral/fileexchange/23661-violin-plots | What's the simplest way to create a beanplot in MATLAB? [closed]
I found an excellent series of plot tools on Matlab File Exchange that creates bean plots among several other distribution plots.
http://www.mathworks.com/matlabcentral/fileexchange/23661-violin-plots-for-plotting-multiple-distributions-distributionplot-m | What's the simplest way to create a beanplot in MATLAB? [closed]
I found an excellent series of plot tools on Matlab File Exchange that creates bean plots among several other distribution plots.
http://www.mathworks.com/matlabcentral/fileexchange/23661-violin-plots |
43,273 | What's the simplest way to create a beanplot in MATLAB? [closed] | I know this isn't necessarily what you are looking for, but I suggested to the "improvement request" folks at my work that they ask for variation on the bean plot as option for variability plots in one of MATLAB's competitors. We are a relatively big customer so a useful proportion of our requests actually do get impl... | What's the simplest way to create a beanplot in MATLAB? [closed] | I know this isn't necessarily what you are looking for, but I suggested to the "improvement request" folks at my work that they ask for variation on the bean plot as option for variability plots in on | What's the simplest way to create a beanplot in MATLAB? [closed]
I know this isn't necessarily what you are looking for, but I suggested to the "improvement request" folks at my work that they ask for variation on the bean plot as option for variability plots in one of MATLAB's competitors. We are a relatively big cus... | What's the simplest way to create a beanplot in MATLAB? [closed]
I know this isn't necessarily what you are looking for, but I suggested to the "improvement request" folks at my work that they ask for variation on the bean plot as option for variability plots in on |
43,274 | How to best display forecast deviation? | After looking at your sample data (and assuming its fairly representative of your actual data), the thing that jumped out was the relatively low actual traffic value, regardless of forecast deviation. So, you could consider two charts to show your data:
Chart of actual traffic
Chart of actual deviation (not percentag... | How to best display forecast deviation? | After looking at your sample data (and assuming its fairly representative of your actual data), the thing that jumped out was the relatively low actual traffic value, regardless of forecast deviation. | How to best display forecast deviation?
After looking at your sample data (and assuming its fairly representative of your actual data), the thing that jumped out was the relatively low actual traffic value, regardless of forecast deviation. So, you could consider two charts to show your data:
Chart of actual traffic
... | How to best display forecast deviation?
After looking at your sample data (and assuming its fairly representative of your actual data), the thing that jumped out was the relatively low actual traffic value, regardless of forecast deviation. |
43,275 | Multivariate time series model evaluation with conditional moments | There are some methods out there for two sample testing of covariance matrices but no one has specifically looked at testing for conditional covariance matrices. Are you interested in an overall differences between covariances or differences in specific rows of the covariance matrices or recovering the exact support of... | Multivariate time series model evaluation with conditional moments | There are some methods out there for two sample testing of covariance matrices but no one has specifically looked at testing for conditional covariance matrices. Are you interested in an overall diffe | Multivariate time series model evaluation with conditional moments
There are some methods out there for two sample testing of covariance matrices but no one has specifically looked at testing for conditional covariance matrices. Are you interested in an overall differences between covariances or differences in specific... | Multivariate time series model evaluation with conditional moments
There are some methods out there for two sample testing of covariance matrices but no one has specifically looked at testing for conditional covariance matrices. Are you interested in an overall diffe |
43,276 | Incremental learning methods in R | I'd suggest starting out by taking a look at MOA (Massive Online Analysis) from the University of Waikato in New Zealand. This is the same group behind Weka. (As an aside both Moa and Weka are New Zealand native species.... though the former is now extinct...)
https://moa.cms.waikato.ac.nz/
"MOA is the most popular ope... | Incremental learning methods in R | I'd suggest starting out by taking a look at MOA (Massive Online Analysis) from the University of Waikato in New Zealand. This is the same group behind Weka. (As an aside both Moa and Weka are New Zea | Incremental learning methods in R
I'd suggest starting out by taking a look at MOA (Massive Online Analysis) from the University of Waikato in New Zealand. This is the same group behind Weka. (As an aside both Moa and Weka are New Zealand native species.... though the former is now extinct...)
https://moa.cms.waikato.a... | Incremental learning methods in R
I'd suggest starting out by taking a look at MOA (Massive Online Analysis) from the University of Waikato in New Zealand. This is the same group behind Weka. (As an aside both Moa and Weka are New Zea |
43,277 | Proofs of the central limit theorem | As I recall in this version the random variables are independent with finite variances but the variance need not all be the same. The CLT result holds under a somewhat complicated condition called the Lindeberg condition and the traditional proofs use transform methods.
But the proof we learned was probabilistic. It ... | Proofs of the central limit theorem | As I recall in this version the random variables are independent with finite variances but the variance need not all be the same. The CLT result holds under a somewhat complicated condition called th | Proofs of the central limit theorem
As I recall in this version the random variables are independent with finite variances but the variance need not all be the same. The CLT result holds under a somewhat complicated condition called the Lindeberg condition and the traditional proofs use transform methods.
But the proo... | Proofs of the central limit theorem
As I recall in this version the random variables are independent with finite variances but the variance need not all be the same. The CLT result holds under a somewhat complicated condition called th |
43,278 | Data entry tool for sparse table | +1 for the question. I have not searched the web a lot for existing tools (presumably you did before posting your question here), but I am guessing someone would have to create a GUI to submit data like you want.
You need to consider what kind of format you want to work with elsewhere in your analysis though, because ... | Data entry tool for sparse table | +1 for the question. I have not searched the web a lot for existing tools (presumably you did before posting your question here), but I am guessing someone would have to create a GUI to submit data li | Data entry tool for sparse table
+1 for the question. I have not searched the web a lot for existing tools (presumably you did before posting your question here), but I am guessing someone would have to create a GUI to submit data like you want.
You need to consider what kind of format you want to work with elsewhere ... | Data entry tool for sparse table
+1 for the question. I have not searched the web a lot for existing tools (presumably you did before posting your question here), but I am guessing someone would have to create a GUI to submit data li |
43,279 | Data entry tool for sparse table | Software that is designed for "CATI" (computer assisted telephone interviewing) is usually very good for fast keyboard based data entry. "CfMC" is an example.
The way you could design this in a CATI-type data collection program would be to have one multi-select question with 300 options. The data entry operator would s... | Data entry tool for sparse table | Software that is designed for "CATI" (computer assisted telephone interviewing) is usually very good for fast keyboard based data entry. "CfMC" is an example.
The way you could design this in a CATI-t | Data entry tool for sparse table
Software that is designed for "CATI" (computer assisted telephone interviewing) is usually very good for fast keyboard based data entry. "CfMC" is an example.
The way you could design this in a CATI-type data collection program would be to have one multi-select question with 300 options... | Data entry tool for sparse table
Software that is designed for "CATI" (computer assisted telephone interviewing) is usually very good for fast keyboard based data entry. "CfMC" is an example.
The way you could design this in a CATI-t |
43,280 | Do variations of ELO system exist for non-mirror games? | In our ranking system rankade you can build hybrid groups with both regular and ghost users. In your Starcraft example you should have as many actual players as in your playing group and three ghost (Terran, Zerg, Protoss). Then, you should record every match as a 2-on-2 match, building each faction with an actual play... | Do variations of ELO system exist for non-mirror games? | In our ranking system rankade you can build hybrid groups with both regular and ghost users. In your Starcraft example you should have as many actual players as in your playing group and three ghost ( | Do variations of ELO system exist for non-mirror games?
In our ranking system rankade you can build hybrid groups with both regular and ghost users. In your Starcraft example you should have as many actual players as in your playing group and three ghost (Terran, Zerg, Protoss). Then, you should record every match as a... | Do variations of ELO system exist for non-mirror games?
In our ranking system rankade you can build hybrid groups with both regular and ghost users. In your Starcraft example you should have as many actual players as in your playing group and three ghost ( |
43,281 | How to show that polar coordinates in a uniform distribution on a disk are independent? | The solution may be a bit quirky, with a lot of variables, but it works fine for me.
We know that $X$,$Y$ - random variables in $\mathbb{R}^n$ and $\mathbb{R}^m$ are independent iff
$$\mathbb{E}(\varphi (X) \psi (Y) )= \mathbb{E}(\varphi(X)) \cdot \mathbb{E}(\psi(Y)) $$
$ \forall \varphi \in C^{\infty}_{0} $, $ \forall... | How to show that polar coordinates in a uniform distribution on a disk are independent? | The solution may be a bit quirky, with a lot of variables, but it works fine for me.
We know that $X$,$Y$ - random variables in $\mathbb{R}^n$ and $\mathbb{R}^m$ are independent iff
$$\mathbb{E}(\varp | How to show that polar coordinates in a uniform distribution on a disk are independent?
The solution may be a bit quirky, with a lot of variables, but it works fine for me.
We know that $X$,$Y$ - random variables in $\mathbb{R}^n$ and $\mathbb{R}^m$ are independent iff
$$\mathbb{E}(\varphi (X) \psi (Y) )= \mathbb{E}(\v... | How to show that polar coordinates in a uniform distribution on a disk are independent?
The solution may be a bit quirky, with a lot of variables, but it works fine for me.
We know that $X$,$Y$ - random variables in $\mathbb{R}^n$ and $\mathbb{R}^m$ are independent iff
$$\mathbb{E}(\varp |
43,282 | Negative weights in a moving average? | Summary
The weights are selected to achieve a mathematical end. In Spencer's case, the goal is to allow cubic trends to pass through the filter undistorted. This means that if we decompose the input $X_t$ into a deterministic polynomial trend component $m(t) \equiv c_3 t^3 + c_2 t^2 + c_1 t + c_0$ and a centered stocha... | Negative weights in a moving average? | Summary
The weights are selected to achieve a mathematical end. In Spencer's case, the goal is to allow cubic trends to pass through the filter undistorted. This means that if we decompose the input $ | Negative weights in a moving average?
Summary
The weights are selected to achieve a mathematical end. In Spencer's case, the goal is to allow cubic trends to pass through the filter undistorted. This means that if we decompose the input $X_t$ into a deterministic polynomial trend component $m(t) \equiv c_3 t^3 + c_2 t^... | Negative weights in a moving average?
Summary
The weights are selected to achieve a mathematical end. In Spencer's case, the goal is to allow cubic trends to pass through the filter undistorted. This means that if we decompose the input $ |
43,283 | SVAR, Cholesky decomposition and impulse-response function in R | Take a look at the package vars in R. | SVAR, Cholesky decomposition and impulse-response function in R | Take a look at the package vars in R. | SVAR, Cholesky decomposition and impulse-response function in R
Take a look at the package vars in R. | SVAR, Cholesky decomposition and impulse-response function in R
Take a look at the package vars in R. |
43,284 | Sampling variance is reduced when removing duplicates -- why? | I don't understand the motivation for removing duplicates and using the HT estimator on the particular probabilities you are using.
More appropriate is to accept that the drawing is with replacement and hence there are duplicates (why would this be a problem? - normally it makes things simpler); and use the correct pro... | Sampling variance is reduced when removing duplicates -- why? | I don't understand the motivation for removing duplicates and using the HT estimator on the particular probabilities you are using.
More appropriate is to accept that the drawing is with replacement a | Sampling variance is reduced when removing duplicates -- why?
I don't understand the motivation for removing duplicates and using the HT estimator on the particular probabilities you are using.
More appropriate is to accept that the drawing is with replacement and hence there are duplicates (why would this be a problem... | Sampling variance is reduced when removing duplicates -- why?
I don't understand the motivation for removing duplicates and using the HT estimator on the particular probabilities you are using.
More appropriate is to accept that the drawing is with replacement a |
43,285 | How can I find out if a subset of Stack Exchange users increase/decrease their post rate based on badges earned? | In order to fully understand my answer and the references that I will provide, I will first (informally) introduce some concepts related to biology - most of the techniques that I will refer to are used in computational biology and therefore most of the reference you will find will assume a basic familiarity with the t... | How can I find out if a subset of Stack Exchange users increase/decrease their post rate based on ba | In order to fully understand my answer and the references that I will provide, I will first (informally) introduce some concepts related to biology - most of the techniques that I will refer to are us | How can I find out if a subset of Stack Exchange users increase/decrease their post rate based on badges earned?
In order to fully understand my answer and the references that I will provide, I will first (informally) introduce some concepts related to biology - most of the techniques that I will refer to are used in c... | How can I find out if a subset of Stack Exchange users increase/decrease their post rate based on ba
In order to fully understand my answer and the references that I will provide, I will first (informally) introduce some concepts related to biology - most of the techniques that I will refer to are us |
43,286 | Sum of lognormal distributed insurance claims | Firstly, the Kolmogorov-Smirnov is a test for a completely specified distribution. If you estimate the parameters rather than pre-specify them, the test doesn't have the intended properties - in particular, it is much less likely to reject the null, either when it's true or when it's false.
You simply can't use it with... | Sum of lognormal distributed insurance claims | Firstly, the Kolmogorov-Smirnov is a test for a completely specified distribution. If you estimate the parameters rather than pre-specify them, the test doesn't have the intended properties - in parti | Sum of lognormal distributed insurance claims
Firstly, the Kolmogorov-Smirnov is a test for a completely specified distribution. If you estimate the parameters rather than pre-specify them, the test doesn't have the intended properties - in particular, it is much less likely to reject the null, either when it's true or... | Sum of lognormal distributed insurance claims
Firstly, the Kolmogorov-Smirnov is a test for a completely specified distribution. If you estimate the parameters rather than pre-specify them, the test doesn't have the intended properties - in parti |
43,287 | How does pooling and resampling affect variance of sample mean? | Assume that each $X_n$ is distributed with mean $\mu_n$ and variance
$\sigma^2_n$, for $n=1,..., N$.
For each $n$, we draw a sample of size $K_n$, denoted by $\hat{X}_{n,k}$,
$k=1,...,K_n$. Let $M=\sum_{n=1}^N K_n$.
Then, for each sample we obtain the mean
$$
\bar{\hat{X}}_{n} = \frac{1}{K_n} \sum_{k=1}^{K_n} \hat{X}... | How does pooling and resampling affect variance of sample mean? | Assume that each $X_n$ is distributed with mean $\mu_n$ and variance
$\sigma^2_n$, for $n=1,..., N$.
For each $n$, we draw a sample of size $K_n$, denoted by $\hat{X}_{n,k}$,
$k=1,...,K_n$. Let $M=\ | How does pooling and resampling affect variance of sample mean?
Assume that each $X_n$ is distributed with mean $\mu_n$ and variance
$\sigma^2_n$, for $n=1,..., N$.
For each $n$, we draw a sample of size $K_n$, denoted by $\hat{X}_{n,k}$,
$k=1,...,K_n$. Let $M=\sum_{n=1}^N K_n$.
Then, for each sample we obtain the me... | How does pooling and resampling affect variance of sample mean?
Assume that each $X_n$ is distributed with mean $\mu_n$ and variance
$\sigma^2_n$, for $n=1,..., N$.
For each $n$, we draw a sample of size $K_n$, denoted by $\hat{X}_{n,k}$,
$k=1,...,K_n$. Let $M=\ |
43,288 | Incorporating a treatment into a classification scheme | You might try some tree based models, such as randomForest or GBM in R. Both models are good at picking up non-linear effects and interactions, and both also produce variable importance measures that will probably be useful in your analysis.
GBM in particular might be useful, as it fits each successive tree to the res... | Incorporating a treatment into a classification scheme | You might try some tree based models, such as randomForest or GBM in R. Both models are good at picking up non-linear effects and interactions, and both also produce variable importance measures that | Incorporating a treatment into a classification scheme
You might try some tree based models, such as randomForest or GBM in R. Both models are good at picking up non-linear effects and interactions, and both also produce variable importance measures that will probably be useful in your analysis.
GBM in particular migh... | Incorporating a treatment into a classification scheme
You might try some tree based models, such as randomForest or GBM in R. Both models are good at picking up non-linear effects and interactions, and both also produce variable importance measures that |
43,289 | Incorporating a treatment into a classification scheme | The functional form of the model is going to be very important here. In fact there might be interaction effects between the treatments (sensitivity of breaking to bending might depend on whether it has been put through fire before) and hence you need to use a non-linear functional form
So, instead of a form like: $$y=\... | Incorporating a treatment into a classification scheme | The functional form of the model is going to be very important here. In fact there might be interaction effects between the treatments (sensitivity of breaking to bending might depend on whether it ha | Incorporating a treatment into a classification scheme
The functional form of the model is going to be very important here. In fact there might be interaction effects between the treatments (sensitivity of breaking to bending might depend on whether it has been put through fire before) and hence you need to use a non-l... | Incorporating a treatment into a classification scheme
The functional form of the model is going to be very important here. In fact there might be interaction effects between the treatments (sensitivity of breaking to bending might depend on whether it ha |
43,290 | How can I test for a difference between ordered groups? | Spearman correlation is fine as far as it goes, but don't stop there. What if there is a nonlinear relationship? E.g., perhaps the cost difference between those who rate the toy bad vs. medium is not comparable to the cost difference between those who rate it medium vs. good. An ANOVA would help you detect this. Th... | How can I test for a difference between ordered groups? | Spearman correlation is fine as far as it goes, but don't stop there. What if there is a nonlinear relationship? E.g., perhaps the cost difference between those who rate the toy bad vs. medium is no | How can I test for a difference between ordered groups?
Spearman correlation is fine as far as it goes, but don't stop there. What if there is a nonlinear relationship? E.g., perhaps the cost difference between those who rate the toy bad vs. medium is not comparable to the cost difference between those who rate it me... | How can I test for a difference between ordered groups?
Spearman correlation is fine as far as it goes, but don't stop there. What if there is a nonlinear relationship? E.g., perhaps the cost difference between those who rate the toy bad vs. medium is no |
43,291 | How can I test for a difference between ordered groups? | Looks like Page's trend test would suit you well - it could help you to test hypothesis $H_0\colon m_{good}=m_{medium}=m_{bad}, $ where $m $ is a measure of central tendency of estimated cost, against the ordered alternative $H_1\colon m_{good}>m_{medium}>m_{bad}. $ It also nonparametric, so you don't have to assume ... | How can I test for a difference between ordered groups? | Looks like Page's trend test would suit you well - it could help you to test hypothesis $H_0\colon m_{good}=m_{medium}=m_{bad}, $ where $m $ is a measure of central tendency of estimated cost, agains | How can I test for a difference between ordered groups?
Looks like Page's trend test would suit you well - it could help you to test hypothesis $H_0\colon m_{good}=m_{medium}=m_{bad}, $ where $m $ is a measure of central tendency of estimated cost, against the ordered alternative $H_1\colon m_{good}>m_{medium}>m_{bad}... | How can I test for a difference between ordered groups?
Looks like Page's trend test would suit you well - it could help you to test hypothesis $H_0\colon m_{good}=m_{medium}=m_{bad}, $ where $m $ is a measure of central tendency of estimated cost, agains |
43,292 | Subtree replacement vs subtree raising | My knee jerk response is:
without a measure of goodness the word "better" has no meaning.
I am trending to "c, none of the above" as my preferred approach
The approach to "build", if you mean build = grow, is to split on the best leaf until a stopping criterion is breached.
Like all Machine Learning tools, there are ... | Subtree replacement vs subtree raising | My knee jerk response is:
without a measure of goodness the word "better" has no meaning.
I am trending to "c, none of the above" as my preferred approach
The approach to "build", if you mean build | Subtree replacement vs subtree raising
My knee jerk response is:
without a measure of goodness the word "better" has no meaning.
I am trending to "c, none of the above" as my preferred approach
The approach to "build", if you mean build = grow, is to split on the best leaf until a stopping criterion is breached.
Like... | Subtree replacement vs subtree raising
My knee jerk response is:
without a measure of goodness the word "better" has no meaning.
I am trending to "c, none of the above" as my preferred approach
The approach to "build", if you mean build |
43,293 | Minimum-Distance estimation of mixed/mixture distributions | Due to the convoluted nature of the $\alpha$-stable distributions (no likelihood), I would suggest using an ABC technique to estimate the mixture. Peters et al. have a recent paper on this. (Here are some slides from Gareth Peters, as well.) Barthelmé and Chopin use a slightly different technique based on expectation-p... | Minimum-Distance estimation of mixed/mixture distributions | Due to the convoluted nature of the $\alpha$-stable distributions (no likelihood), I would suggest using an ABC technique to estimate the mixture. Peters et al. have a recent paper on this. (Here are | Minimum-Distance estimation of mixed/mixture distributions
Due to the convoluted nature of the $\alpha$-stable distributions (no likelihood), I would suggest using an ABC technique to estimate the mixture. Peters et al. have a recent paper on this. (Here are some slides from Gareth Peters, as well.) Barthelmé and Chopi... | Minimum-Distance estimation of mixed/mixture distributions
Due to the convoluted nature of the $\alpha$-stable distributions (no likelihood), I would suggest using an ABC technique to estimate the mixture. Peters et al. have a recent paper on this. (Here are |
43,294 | How to adjust for a mid-study change in diagnostic protocol? | based on the question and comments, can you make an adjustment so that you deflate the number of cases under the new protocol to what they "would" have had under the old protocol? If you know the difference in the specificity and the sensitivity for both diagnostic tests, and you know the number of patients that the tw... | How to adjust for a mid-study change in diagnostic protocol? | based on the question and comments, can you make an adjustment so that you deflate the number of cases under the new protocol to what they "would" have had under the old protocol? If you know the diff | How to adjust for a mid-study change in diagnostic protocol?
based on the question and comments, can you make an adjustment so that you deflate the number of cases under the new protocol to what they "would" have had under the old protocol? If you know the difference in the specificity and the sensitivity for both diag... | How to adjust for a mid-study change in diagnostic protocol?
based on the question and comments, can you make an adjustment so that you deflate the number of cases under the new protocol to what they "would" have had under the old protocol? If you know the diff |
43,295 | Power needed to detect an interaction | You need a significantly larger sample size to detect an effect for an interaction. To detect an effect of size $d$ for an interact, you need a sample size that is about 4 times larger than the sample size required to detect a main effect of size $d$.
This is because for the interaction you're essentially taking the di... | Power needed to detect an interaction | You need a significantly larger sample size to detect an effect for an interaction. To detect an effect of size $d$ for an interact, you need a sample size that is about 4 times larger than the sample | Power needed to detect an interaction
You need a significantly larger sample size to detect an effect for an interaction. To detect an effect of size $d$ for an interact, you need a sample size that is about 4 times larger than the sample size required to detect a main effect of size $d$.
This is because for the intera... | Power needed to detect an interaction
You need a significantly larger sample size to detect an effect for an interaction. To detect an effect of size $d$ for an interact, you need a sample size that is about 4 times larger than the sample |
43,296 | Power needed to detect an interaction | If we use the question by Macro:
"To achieve the same power, does one require a greater sample size when testing an interaction than when testing a main effect?"
It does not necessarily need a bigger sample with "that" many in each dummy variable category, but you have to be aware of the problem of multicollinearity,... | Power needed to detect an interaction | If we use the question by Macro:
"To achieve the same power, does one require a greater sample size when testing an interaction than when testing a main effect?"
It does not necessarily need a bigge | Power needed to detect an interaction
If we use the question by Macro:
"To achieve the same power, does one require a greater sample size when testing an interaction than when testing a main effect?"
It does not necessarily need a bigger sample with "that" many in each dummy variable category, but you have to be awar... | Power needed to detect an interaction
If we use the question by Macro:
"To achieve the same power, does one require a greater sample size when testing an interaction than when testing a main effect?"
It does not necessarily need a bigge |
43,297 | Using a histogram to estimate class label densities in a tree learner | I didn't understand very well what you want to do with histograms.
If you have a set of features $R_j$ and you are able to generate a split $s$ you can compute $Q(R_j,s)$ easily.Given the split you directly have the two subset of records $R_{jls}$ and $R_{jrs}$ and you need only to compute the probability distribution... | Using a histogram to estimate class label densities in a tree learner | I didn't understand very well what you want to do with histograms.
If you have a set of features $R_j$ and you are able to generate a split $s$ you can compute $Q(R_j,s)$ easily.Given the split you d | Using a histogram to estimate class label densities in a tree learner
I didn't understand very well what you want to do with histograms.
If you have a set of features $R_j$ and you are able to generate a split $s$ you can compute $Q(R_j,s)$ easily.Given the split you directly have the two subset of records $R_{jls}$ a... | Using a histogram to estimate class label densities in a tree learner
I didn't understand very well what you want to do with histograms.
If you have a set of features $R_j$ and you are able to generate a split $s$ you can compute $Q(R_j,s)$ easily.Given the split you d |
43,298 | Twitter data and regression time series | You can use an ARMAX Model to relate the amount of new followers (y) to the number of tweets per day (x). This model will suggest the appropriate delay and response mechanism. Care should be taken to ensure that outiliers/level shifts/local time trends are correctly identified and incorporated. There may also be the ne... | Twitter data and regression time series | You can use an ARMAX Model to relate the amount of new followers (y) to the number of tweets per day (x). This model will suggest the appropriate delay and response mechanism. Care should be taken to | Twitter data and regression time series
You can use an ARMAX Model to relate the amount of new followers (y) to the number of tweets per day (x). This model will suggest the appropriate delay and response mechanism. Care should be taken to ensure that outiliers/level shifts/local time trends are correctly identified an... | Twitter data and regression time series
You can use an ARMAX Model to relate the amount of new followers (y) to the number of tweets per day (x). This model will suggest the appropriate delay and response mechanism. Care should be taken to |
43,299 | Combining n-grams | Not sure if this is what you're looking for, but you might want to look at Katz backoff. This entails training vanilla n-gram models for $1 \le n \le N$, then estimating probabilities for large n by "backing off" to an (n-1)-gram model when the n-gram in question was not observed more often than some frequency threshol... | Combining n-grams | Not sure if this is what you're looking for, but you might want to look at Katz backoff. This entails training vanilla n-gram models for $1 \le n \le N$, then estimating probabilities for large n by " | Combining n-grams
Not sure if this is what you're looking for, but you might want to look at Katz backoff. This entails training vanilla n-gram models for $1 \le n \le N$, then estimating probabilities for large n by "backing off" to an (n-1)-gram model when the n-gram in question was not observed more often than some ... | Combining n-grams
Not sure if this is what you're looking for, but you might want to look at Katz backoff. This entails training vanilla n-gram models for $1 \le n \le N$, then estimating probabilities for large n by " |
43,300 | One standard error rule for variable selection | Isn't it as simple as calculating error of mean of $R'[T_i]$ (for a given i) using each cross validation fold as an "independent" measurement? (i.e. calculating standard deviation of $R'[T_i]$ (across K folds) and then dividing by $\sqrt{K-1}$ gives a reasonable resampling-based proxy of that standard error) | One standard error rule for variable selection | Isn't it as simple as calculating error of mean of $R'[T_i]$ (for a given i) using each cross validation fold as an "independent" measurement? (i.e. calculating standard deviation of $R'[T_i]$ (across | One standard error rule for variable selection
Isn't it as simple as calculating error of mean of $R'[T_i]$ (for a given i) using each cross validation fold as an "independent" measurement? (i.e. calculating standard deviation of $R'[T_i]$ (across K folds) and then dividing by $\sqrt{K-1}$ gives a reasonable resampling... | One standard error rule for variable selection
Isn't it as simple as calculating error of mean of $R'[T_i]$ (for a given i) using each cross validation fold as an "independent" measurement? (i.e. calculating standard deviation of $R'[T_i]$ (across |
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