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 |
|---|---|---|---|---|---|---|
35,201 | Does ARIMA require normally distributed errors or normally distributed input data? | To answer your first question and to quote from [1],
Mathematics tell us that linear predictor can be optimal only when the
process is Gaussian. When the process is non-Gaussian, a better
predictor may be given by a non-linear dynamic model (Masani and
Wiener, 1959).
Why statisticians, both young and old, ke... | Does ARIMA require normally distributed errors or normally distributed input data? | To answer your first question and to quote from [1],
Mathematics tell us that linear predictor can be optimal only when the
process is Gaussian. When the process is non-Gaussian, a better
predic | Does ARIMA require normally distributed errors or normally distributed input data?
To answer your first question and to quote from [1],
Mathematics tell us that linear predictor can be optimal only when the
process is Gaussian. When the process is non-Gaussian, a better
predictor may be given by a non-linear dyn... | Does ARIMA require normally distributed errors or normally distributed input data?
To answer your first question and to quote from [1],
Mathematics tell us that linear predictor can be optimal only when the
process is Gaussian. When the process is non-Gaussian, a better
predic |
35,202 | Significance of dummy variables in regression | Categorical variables can be represented several different ways in a regression model. The most common, by far, is reference cell coding. From your description (and my prior), I suspect that is what was used in your case. The standard statistical output will give you two tests. Let's say that A is the reference lev... | Significance of dummy variables in regression | Categorical variables can be represented several different ways in a regression model. The most common, by far, is reference cell coding. From your description (and my prior), I suspect that is what | Significance of dummy variables in regression
Categorical variables can be represented several different ways in a regression model. The most common, by far, is reference cell coding. From your description (and my prior), I suspect that is what was used in your case. The standard statistical output will give you two... | Significance of dummy variables in regression
Categorical variables can be represented several different ways in a regression model. The most common, by far, is reference cell coding. From your description (and my prior), I suspect that is what |
35,203 | Significance of dummy variables in regression | There is no need to include indicator variables for each of the categories. Let's say category A is coming out significant. Your results are suggesting that you consider collapsing the categories into "category A" and "all other categories".
Of course, you should perform an F-test for nested model vs. full model to che... | Significance of dummy variables in regression | There is no need to include indicator variables for each of the categories. Let's say category A is coming out significant. Your results are suggesting that you consider collapsing the categories into | Significance of dummy variables in regression
There is no need to include indicator variables for each of the categories. Let's say category A is coming out significant. Your results are suggesting that you consider collapsing the categories into "category A" and "all other categories".
Of course, you should perform an... | Significance of dummy variables in regression
There is no need to include indicator variables for each of the categories. Let's say category A is coming out significant. Your results are suggesting that you consider collapsing the categories into |
35,204 | Categorical response variable prediction | You could use ANY classifier. Including Linear Discriminants, multinomial logit as Bill pointed out, Support Vector Machines, Neural Nets, CART, random forest, C5 trees, there are a world of different models that can help you predict $v.a$ using $v.b$ and $v.c$. Here is an example using the R implementation of random f... | Categorical response variable prediction | You could use ANY classifier. Including Linear Discriminants, multinomial logit as Bill pointed out, Support Vector Machines, Neural Nets, CART, random forest, C5 trees, there are a world of different | Categorical response variable prediction
You could use ANY classifier. Including Linear Discriminants, multinomial logit as Bill pointed out, Support Vector Machines, Neural Nets, CART, random forest, C5 trees, there are a world of different models that can help you predict $v.a$ using $v.b$ and $v.c$. Here is an examp... | Categorical response variable prediction
You could use ANY classifier. Including Linear Discriminants, multinomial logit as Bill pointed out, Support Vector Machines, Neural Nets, CART, random forest, C5 trees, there are a world of different |
35,205 | Categorical response variable prediction | This is a more a partial practical answer, but it works for me to do some exercises before getting deeply into theory.
This ats.ucla.edu link is a reference that might help beggining to understand about multinomial logistic regression (as pointed out by Bill), in a more practical way.
It presents reproducible code to... | Categorical response variable prediction | This is a more a partial practical answer, but it works for me to do some exercises before getting deeply into theory.
This ats.ucla.edu link is a reference that might help beggining to understand a | Categorical response variable prediction
This is a more a partial practical answer, but it works for me to do some exercises before getting deeply into theory.
This ats.ucla.edu link is a reference that might help beggining to understand about multinomial logistic regression (as pointed out by Bill), in a more practi... | Categorical response variable prediction
This is a more a partial practical answer, but it works for me to do some exercises before getting deeply into theory.
This ats.ucla.edu link is a reference that might help beggining to understand a |
35,206 | Integral of random variable | Doesn't make much sense. Remember that $C$ is a (measurable) map from $\Omega$ (the underlying sample space) to $\mathbb{R}$. Hence, strictly speaking
$$
\int_a^b C \,dx = \int_a^b C(\omega) \,dx = C(\omega) \int_a^b \,dx = C(\omega) \cdot (b-a) \, .
$$
(P.S. When you have a stochastic process $Z:\Omega\times\mathbb... | Integral of random variable | Doesn't make much sense. Remember that $C$ is a (measurable) map from $\Omega$ (the underlying sample space) to $\mathbb{R}$. Hence, strictly speaking
$$
\int_a^b C \,dx = \int_a^b C(\omega) \,dx = | Integral of random variable
Doesn't make much sense. Remember that $C$ is a (measurable) map from $\Omega$ (the underlying sample space) to $\mathbb{R}$. Hence, strictly speaking
$$
\int_a^b C \,dx = \int_a^b C(\omega) \,dx = C(\omega) \int_a^b \,dx = C(\omega) \cdot (b-a) \, .
$$
(P.S. When you have a stochastic pr... | Integral of random variable
Doesn't make much sense. Remember that $C$ is a (measurable) map from $\Omega$ (the underlying sample space) to $\mathbb{R}$. Hence, strictly speaking
$$
\int_a^b C \,dx = \int_a^b C(\omega) \,dx = |
35,207 | What's the difference between Hedges' g and Cohen's d? [duplicate] | You are right, the difference between them is very small and with large $N$ will disappear. In fact most people (at least in my experience) are not aware of any of this; "Cohen's $d$" is often used generically, many people have not heard of Hedges' $g$, but they use the latter formula and call it by the former name. ... | What's the difference between Hedges' g and Cohen's d? [duplicate] | You are right, the difference between them is very small and with large $N$ will disappear. In fact most people (at least in my experience) are not aware of any of this; "Cohen's $d$" is often used g | What's the difference between Hedges' g and Cohen's d? [duplicate]
You are right, the difference between them is very small and with large $N$ will disappear. In fact most people (at least in my experience) are not aware of any of this; "Cohen's $d$" is often used generically, many people have not heard of Hedges' $g$... | What's the difference between Hedges' g and Cohen's d? [duplicate]
You are right, the difference between them is very small and with large $N$ will disappear. In fact most people (at least in my experience) are not aware of any of this; "Cohen's $d$" is often used g |
35,208 | What do we call multiple testing? | If all aspects of a test are specified in advance and its assumptions are met, you can safely conclude that the null hypothesis will be rejected erroneously at the frequency defined by the error level. If you conduct several tests (a “family” of tests), each of these tests is an additional occasion to commit this error... | What do we call multiple testing? | If all aspects of a test are specified in advance and its assumptions are met, you can safely conclude that the null hypothesis will be rejected erroneously at the frequency defined by the error level | What do we call multiple testing?
If all aspects of a test are specified in advance and its assumptions are met, you can safely conclude that the null hypothesis will be rejected erroneously at the frequency defined by the error level. If you conduct several tests (a “family” of tests), each of these tests is an additi... | What do we call multiple testing?
If all aspects of a test are specified in advance and its assumptions are met, you can safely conclude that the null hypothesis will be rejected erroneously at the frequency defined by the error level |
35,209 | What do we call multiple testing? | Items 3 and 4 on your list appear to be most closely related to the problem of controlling the error rate. I hope someone else will comment on items 1 and 2. I have never quite known how to think of multiple testing of regression coefficients from the same model.
Even if you don't actually test the data, but visually l... | What do we call multiple testing? | Items 3 and 4 on your list appear to be most closely related to the problem of controlling the error rate. I hope someone else will comment on items 1 and 2. I have never quite known how to think of m | What do we call multiple testing?
Items 3 and 4 on your list appear to be most closely related to the problem of controlling the error rate. I hope someone else will comment on items 1 and 2. I have never quite known how to think of multiple testing of regression coefficients from the same model.
Even if you don't actu... | What do we call multiple testing?
Items 3 and 4 on your list appear to be most closely related to the problem of controlling the error rate. I hope someone else will comment on items 1 and 2. I have never quite known how to think of m |
35,210 | What do the "coefficients" in R's HoltWinters function represent? | +1 this is confusing. If your time series has length $N$ and frequency $p$, then the so-called "coefficients" (which can be accessed as HW$coeff if HW is the object returned by HoltWinters) are exactly the values $a[N]$, $b[N]$ and $s[N-p+1]$, $s[N-p+2], \cdots s[N]$ where these are defined by the formulas in the Holt ... | What do the "coefficients" in R's HoltWinters function represent? | +1 this is confusing. If your time series has length $N$ and frequency $p$, then the so-called "coefficients" (which can be accessed as HW$coeff if HW is the object returned by HoltWinters) are exactl | What do the "coefficients" in R's HoltWinters function represent?
+1 this is confusing. If your time series has length $N$ and frequency $p$, then the so-called "coefficients" (which can be accessed as HW$coeff if HW is the object returned by HoltWinters) are exactly the values $a[N]$, $b[N]$ and $s[N-p+1]$, $s[N-p+2],... | What do the "coefficients" in R's HoltWinters function represent?
+1 this is confusing. If your time series has length $N$ and frequency $p$, then the so-called "coefficients" (which can be accessed as HW$coeff if HW is the object returned by HoltWinters) are exactl |
35,211 | What do the "coefficients" in R's HoltWinters function represent? | The meaning of the a, b, s, alpha, beta and gamma parameters is described in the help on the HoltWinters function (try ?HoltWinters in R), under Details.
e.g. the additive model is described so:
Yhat[t+h] = a[t] + h * b[t] + s[t - p + 1 + (h - 1) mod p],
where a[t], b[t] and s[t] are given by
a[t] = α (Y[t] - s[t-p]) +... | What do the "coefficients" in R's HoltWinters function represent? | The meaning of the a, b, s, alpha, beta and gamma parameters is described in the help on the HoltWinters function (try ?HoltWinters in R), under Details.
e.g. the additive model is described so:
Yhat[ | What do the "coefficients" in R's HoltWinters function represent?
The meaning of the a, b, s, alpha, beta and gamma parameters is described in the help on the HoltWinters function (try ?HoltWinters in R), under Details.
e.g. the additive model is described so:
Yhat[t+h] = a[t] + h * b[t] + s[t - p + 1 + (h - 1) mod p],... | What do the "coefficients" in R's HoltWinters function represent?
The meaning of the a, b, s, alpha, beta and gamma parameters is described in the help on the HoltWinters function (try ?HoltWinters in R), under Details.
e.g. the additive model is described so:
Yhat[ |
35,212 | What do the "coefficients" in R's HoltWinters function represent? | I agree that there is puzzle. To see the puzzle I considered
the co2 series available in R. The answer is long. May you just to the *-part that I added today
I have expected that
co2HWBis$coefficients[2]
equals
co2HW$fitted[length(co2HW$fitted[,3]),3]
i.e. the coeff equals the last extimated trend.
Below you can ch... | What do the "coefficients" in R's HoltWinters function represent? | I agree that there is puzzle. To see the puzzle I considered
the co2 series available in R. The answer is long. May you just to the *-part that I added today
I have expected that
co2HWBis$coefficient | What do the "coefficients" in R's HoltWinters function represent?
I agree that there is puzzle. To see the puzzle I considered
the co2 series available in R. The answer is long. May you just to the *-part that I added today
I have expected that
co2HWBis$coefficients[2]
equals
co2HW$fitted[length(co2HW$fitted[,3]),3]... | What do the "coefficients" in R's HoltWinters function represent?
I agree that there is puzzle. To see the puzzle I considered
the co2 series available in R. The answer is long. May you just to the *-part that I added today
I have expected that
co2HWBis$coefficient |
35,213 | What do the "coefficients" in R's HoltWinters function represent? | I think the key point about the coefficients, which I couldn't see in the other answers but may have missed, is that they are the values of smoothed level and smoothed trend for the last period in the time series on which the forecast was based/made; and smoothed seasonal components for the last 12 months of that tim... | What do the "coefficients" in R's HoltWinters function represent? | I think the key point about the coefficients, which I couldn't see in the other answers but may have missed, is that they are the values of smoothed level and smoothed trend for the last period in th | What do the "coefficients" in R's HoltWinters function represent?
I think the key point about the coefficients, which I couldn't see in the other answers but may have missed, is that they are the values of smoothed level and smoothed trend for the last period in the time series on which the forecast was based/made; a... | What do the "coefficients" in R's HoltWinters function represent?
I think the key point about the coefficients, which I couldn't see in the other answers but may have missed, is that they are the values of smoothed level and smoothed trend for the last period in th |
35,214 | What do the "coefficients" in R's HoltWinters function represent? | This is from the HoltWinters documentation in R. I had the same question and this answers why I could not calculate the same seasonal values. The function is using a decomposition method to find all the initial values when incorporating seasonality, whereas for single and double exponential smoothing it doesn't do this... | What do the "coefficients" in R's HoltWinters function represent? | This is from the HoltWinters documentation in R. I had the same question and this answers why I could not calculate the same seasonal values. The function is using a decomposition method to find all t | What do the "coefficients" in R's HoltWinters function represent?
This is from the HoltWinters documentation in R. I had the same question and this answers why I could not calculate the same seasonal values. The function is using a decomposition method to find all the initial values when incorporating seasonality, wher... | What do the "coefficients" in R's HoltWinters function represent?
This is from the HoltWinters documentation in R. I had the same question and this answers why I could not calculate the same seasonal values. The function is using a decomposition method to find all t |
35,215 | What do the "coefficients" in R's HoltWinters function represent? | Coefficients in Holtwinter's Method
In the HoltWinter's model with seasonal= 'additive', I am able to produce the model output for coefficients manually. It seems like we need to use a[N-p],b[N-p] instead of a[N-1],b[N-1] in the HW formula. This may be because 'a' and 'b' depend on 's' component. I think the coefficie... | What do the "coefficients" in R's HoltWinters function represent? | Coefficients in Holtwinter's Method
In the HoltWinter's model with seasonal= 'additive', I am able to produce the model output for coefficients manually. It seems like we need to use a[N-p],b[N-p] in | What do the "coefficients" in R's HoltWinters function represent?
Coefficients in Holtwinter's Method
In the HoltWinter's model with seasonal= 'additive', I am able to produce the model output for coefficients manually. It seems like we need to use a[N-p],b[N-p] instead of a[N-1],b[N-1] in the HW formula. This may be ... | What do the "coefficients" in R's HoltWinters function represent?
Coefficients in Holtwinter's Method
In the HoltWinter's model with seasonal= 'additive', I am able to produce the model output for coefficients manually. It seems like we need to use a[N-p],b[N-p] in |
35,216 | Statistical Difference from Zero | A t-test would be able to test if the average of all the values is different from 0. There is no second set of data, you want a one-sample t-test. In R:
x <- c(1,2,3,4) #PUT YOUR DATA HERE
t.test(x)
would do.
But if you want to test whether the trend is different from 0, that's a different question. How to do that wou... | Statistical Difference from Zero | A t-test would be able to test if the average of all the values is different from 0. There is no second set of data, you want a one-sample t-test. In R:
x <- c(1,2,3,4) #PUT YOUR DATA HERE
t.test(x)
| Statistical Difference from Zero
A t-test would be able to test if the average of all the values is different from 0. There is no second set of data, you want a one-sample t-test. In R:
x <- c(1,2,3,4) #PUT YOUR DATA HERE
t.test(x)
would do.
But if you want to test whether the trend is different from 0, that's a diffe... | Statistical Difference from Zero
A t-test would be able to test if the average of all the values is different from 0. There is no second set of data, you want a one-sample t-test. In R:
x <- c(1,2,3,4) #PUT YOUR DATA HERE
t.test(x)
|
35,217 | Statistical Difference from Zero | No, don't use 0s, use the one sample t-test and test whether the mean differs from 0 (it does).
In R it goes as follows:
x <- c(0.2245, 0.243, 0.2312, 0.1795, 0.1923, 0.17, 0.2025, 0.2059,
0.2394, 0.205, 0.2201, 0.2261, 0.1817, 0.2143, 0.2126, 0.237,
0.1984, 0.228, 0.2292, 0.2236, 0.2096, 0.2258, 0.2155)
> t.test(x)
... | Statistical Difference from Zero | No, don't use 0s, use the one sample t-test and test whether the mean differs from 0 (it does).
In R it goes as follows:
x <- c(0.2245, 0.243, 0.2312, 0.1795, 0.1923, 0.17, 0.2025, 0.2059,
0.2394, 0. | Statistical Difference from Zero
No, don't use 0s, use the one sample t-test and test whether the mean differs from 0 (it does).
In R it goes as follows:
x <- c(0.2245, 0.243, 0.2312, 0.1795, 0.1923, 0.17, 0.2025, 0.2059,
0.2394, 0.205, 0.2201, 0.2261, 0.1817, 0.2143, 0.2126, 0.237,
0.1984, 0.228, 0.2292, 0.2236, 0.2... | Statistical Difference from Zero
No, don't use 0s, use the one sample t-test and test whether the mean differs from 0 (it does).
In R it goes as follows:
x <- c(0.2245, 0.243, 0.2312, 0.1795, 0.1923, 0.17, 0.2025, 0.2059,
0.2394, 0. |
35,218 | Sum standard deviation vs standard error | The sum standard deviation is, as the name suggests, the standard deviation of the sum of $n$ random variables. The standard error you're talking about is just another name for the standard deviation of the mean of $n$ random variables. As you noted, the two formulas are closely related; since the sum of $n$ random var... | Sum standard deviation vs standard error | The sum standard deviation is, as the name suggests, the standard deviation of the sum of $n$ random variables. The standard error you're talking about is just another name for the standard deviation | Sum standard deviation vs standard error
The sum standard deviation is, as the name suggests, the standard deviation of the sum of $n$ random variables. The standard error you're talking about is just another name for the standard deviation of the mean of $n$ random variables. As you noted, the two formulas are closely... | Sum standard deviation vs standard error
The sum standard deviation is, as the name suggests, the standard deviation of the sum of $n$ random variables. The standard error you're talking about is just another name for the standard deviation |
35,219 | Sum standard deviation vs standard error | The first standard deviation formula you gave is the SD for a sum. The standard error is the SD of the sample mean. Remember that:
$\text{Var}(aX)=a^2 \text{Var}(X)$ and the variance of the sum is the sum of the variances (First formula). So
$\text{Var}(\bar{X})=\frac{n\sigma^2}{n^2}=\sigma^2/n$. Taking the square root... | Sum standard deviation vs standard error | The first standard deviation formula you gave is the SD for a sum. The standard error is the SD of the sample mean. Remember that:
$\text{Var}(aX)=a^2 \text{Var}(X)$ and the variance of the sum is the | Sum standard deviation vs standard error
The first standard deviation formula you gave is the SD for a sum. The standard error is the SD of the sample mean. Remember that:
$\text{Var}(aX)=a^2 \text{Var}(X)$ and the variance of the sum is the sum of the variances (First formula). So
$\text{Var}(\bar{X})=\frac{n\sigma^2}... | Sum standard deviation vs standard error
The first standard deviation formula you gave is the SD for a sum. The standard error is the SD of the sample mean. Remember that:
$\text{Var}(aX)=a^2 \text{Var}(X)$ and the variance of the sum is the |
35,220 | Is PCA appropriate when $n<p$? | Yes, you surely can do that. I don’t know applications in ecology, but you may be interested to know that this is widely used in genetics (epidemiology and population genetics), with $n \ll p$, typically $n = 1000$ or $5000$ individuals and $p = 500\,000$ genotypes.
To adjust analyses for population mixture, the first... | Is PCA appropriate when $n<p$? | Yes, you surely can do that. I don’t know applications in ecology, but you may be interested to know that this is widely used in genetics (epidemiology and population genetics), with $n \ll p$, typica | Is PCA appropriate when $n<p$?
Yes, you surely can do that. I don’t know applications in ecology, but you may be interested to know that this is widely used in genetics (epidemiology and population genetics), with $n \ll p$, typically $n = 1000$ or $5000$ individuals and $p = 500\,000$ genotypes.
To adjust analyses fo... | Is PCA appropriate when $n<p$?
Yes, you surely can do that. I don’t know applications in ecology, but you may be interested to know that this is widely used in genetics (epidemiology and population genetics), with $n \ll p$, typica |
35,221 | Filtering using ARMA model in R | I think this does what you want:
library(forecast)
fit <- Arima(x,order=c(1,0,1))
yfiltered <- residuals(Arima(y,model=fit)) | Filtering using ARMA model in R | I think this does what you want:
library(forecast)
fit <- Arima(x,order=c(1,0,1))
yfiltered <- residuals(Arima(y,model=fit)) | Filtering using ARMA model in R
I think this does what you want:
library(forecast)
fit <- Arima(x,order=c(1,0,1))
yfiltered <- residuals(Arima(y,model=fit)) | Filtering using ARMA model in R
I think this does what you want:
library(forecast)
fit <- Arima(x,order=c(1,0,1))
yfiltered <- residuals(Arima(y,model=fit)) |
35,222 | Filtering using ARMA model in R | I suggest three different functions:
stats:::arima
forecast:::Arima
forecast:::auto.arima
forecast:::auto.arima will automatically seelct the p and q lags for you. | Filtering using ARMA model in R | I suggest three different functions:
stats:::arima
forecast:::Arima
forecast:::auto.arima
forecast:::auto.arima will automatically seelct the p and q lags for you. | Filtering using ARMA model in R
I suggest three different functions:
stats:::arima
forecast:::Arima
forecast:::auto.arima
forecast:::auto.arima will automatically seelct the p and q lags for you. | Filtering using ARMA model in R
I suggest three different functions:
stats:::arima
forecast:::Arima
forecast:::auto.arima
forecast:::auto.arima will automatically seelct the p and q lags for you. |
35,223 | Good F1 score for anomaly detection | You may have guessed it already, but it depends on ...
what the data allows you to achieve at maximum. Regarding this, please see Expected best performance possible on a data set
the cost of a wrong prediction and the benefit of a detected anomaly. I recommend The Foundations of Cost-Sensitive Learning by Charles Elka... | Good F1 score for anomaly detection | You may have guessed it already, but it depends on ...
what the data allows you to achieve at maximum. Regarding this, please see Expected best performance possible on a data set
the cost of a wrong | Good F1 score for anomaly detection
You may have guessed it already, but it depends on ...
what the data allows you to achieve at maximum. Regarding this, please see Expected best performance possible on a data set
the cost of a wrong prediction and the benefit of a detected anomaly. I recommend The Foundations of Cos... | Good F1 score for anomaly detection
You may have guessed it already, but it depends on ...
what the data allows you to achieve at maximum. Regarding this, please see Expected best performance possible on a data set
the cost of a wrong |
35,224 | Classification error is lower when I don't do any learning on the dataset? | It is not true that you are not doing any learning. What you are doing is using the well known classification algorithm called Nearest Neighbor (NN). It is important to realize that you are learning as long as you are using the train data (even if you dont explicitly calculate some parameter) - and in this case you are... | Classification error is lower when I don't do any learning on the dataset? | It is not true that you are not doing any learning. What you are doing is using the well known classification algorithm called Nearest Neighbor (NN). It is important to realize that you are learning a | Classification error is lower when I don't do any learning on the dataset?
It is not true that you are not doing any learning. What you are doing is using the well known classification algorithm called Nearest Neighbor (NN). It is important to realize that you are learning as long as you are using the train data (even ... | Classification error is lower when I don't do any learning on the dataset?
It is not true that you are not doing any learning. What you are doing is using the well known classification algorithm called Nearest Neighbor (NN). It is important to realize that you are learning a |
35,225 | Computing a p-value using bootstrap | Great set of alternatives for generating a discussion!
1) Your primary suggestion is actually a form of permutation test rather than a bootstrap, and does indeed generate p-values. The p-values are exact under the assumption of exchangeability, which I can't describe in terms specific to your model as I don't know you... | Computing a p-value using bootstrap | Great set of alternatives for generating a discussion!
1) Your primary suggestion is actually a form of permutation test rather than a bootstrap, and does indeed generate p-values. The p-values are e | Computing a p-value using bootstrap
Great set of alternatives for generating a discussion!
1) Your primary suggestion is actually a form of permutation test rather than a bootstrap, and does indeed generate p-values. The p-values are exact under the assumption of exchangeability, which I can't describe in terms specif... | Computing a p-value using bootstrap
Great set of alternatives for generating a discussion!
1) Your primary suggestion is actually a form of permutation test rather than a bootstrap, and does indeed generate p-values. The p-values are e |
35,226 | How to calculate with tiny probabilities and large samples? | In physics, a Fermi problem is an exercise which asks you to estimate an order of magnitude. You can do the same for probabilities. With practice, your intuition should improve.
As Xi'an commented, you can use logarithms. Perhaps you can't see $2^{2^{25}} \gg 10^{10}$ at a glance, but you can see that $2^{25} \gg 10$ ... | How to calculate with tiny probabilities and large samples? | In physics, a Fermi problem is an exercise which asks you to estimate an order of magnitude. You can do the same for probabilities. With practice, your intuition should improve.
As Xi'an commented, y | How to calculate with tiny probabilities and large samples?
In physics, a Fermi problem is an exercise which asks you to estimate an order of magnitude. You can do the same for probabilities. With practice, your intuition should improve.
As Xi'an commented, you can use logarithms. Perhaps you can't see $2^{2^{25}} \gg... | How to calculate with tiny probabilities and large samples?
In physics, a Fermi problem is an exercise which asks you to estimate an order of magnitude. You can do the same for probabilities. With practice, your intuition should improve.
As Xi'an commented, y |
35,227 | How to calculate with tiny probabilities and large samples? | I think this amounts to a problem of estimating the extreme tails of a probability distribution without the extremely large sample size needed to get any or just a small few number of values observed at those extreme values. The only way to do this is by assuming a parametric model which "automstically" assumes a shap... | How to calculate with tiny probabilities and large samples? | I think this amounts to a problem of estimating the extreme tails of a probability distribution without the extremely large sample size needed to get any or just a small few number of values observed | How to calculate with tiny probabilities and large samples?
I think this amounts to a problem of estimating the extreme tails of a probability distribution without the extremely large sample size needed to get any or just a small few number of values observed at those extreme values. The only way to do this is by assu... | How to calculate with tiny probabilities and large samples?
I think this amounts to a problem of estimating the extreme tails of a probability distribution without the extremely large sample size needed to get any or just a small few number of values observed |
35,228 | Based on factor loadings (in factor analysis) can we give unequal weights to Likert scale items? | Yes, it is possible to supply each item with its own weight. This weight, however, cannot be the loading itself because - you might remember - loading is a regression coefficient of a factor in predicting an item, not vice versa. The weight you imply must be a regression coefficient of an item in predicting a factor. W... | Based on factor loadings (in factor analysis) can we give unequal weights to Likert scale items? | Yes, it is possible to supply each item with its own weight. This weight, however, cannot be the loading itself because - you might remember - loading is a regression coefficient of a factor in predic | Based on factor loadings (in factor analysis) can we give unequal weights to Likert scale items?
Yes, it is possible to supply each item with its own weight. This weight, however, cannot be the loading itself because - you might remember - loading is a regression coefficient of a factor in predicting an item, not vice ... | Based on factor loadings (in factor analysis) can we give unequal weights to Likert scale items?
Yes, it is possible to supply each item with its own weight. This weight, however, cannot be the loading itself because - you might remember - loading is a regression coefficient of a factor in predic |
35,229 | Looking for a good online resource about survey design and analysis | On the more qualitative side of things in survey methodology, see the books mentioned in https://blogs.rti.org/surveypost/2012/05/15/surveying-on-a-deserted-island-a-bakers-dozen-list-of-resources-to-take-along/. If you are totally new to sampling, Lohr (2009) is a more modern treatment covering additionally some of th... | Looking for a good online resource about survey design and analysis | On the more qualitative side of things in survey methodology, see the books mentioned in https://blogs.rti.org/surveypost/2012/05/15/surveying-on-a-deserted-island-a-bakers-dozen-list-of-resources-to- | Looking for a good online resource about survey design and analysis
On the more qualitative side of things in survey methodology, see the books mentioned in https://blogs.rti.org/surveypost/2012/05/15/surveying-on-a-deserted-island-a-bakers-dozen-list-of-resources-to-take-along/. If you are totally new to sampling, Loh... | Looking for a good online resource about survey design and analysis
On the more qualitative side of things in survey methodology, see the books mentioned in https://blogs.rti.org/surveypost/2012/05/15/surveying-on-a-deserted-island-a-bakers-dozen-list-of-resources-to- |
35,230 | Looking for a good online resource about survey design and analysis | I recommend Cochran's Sampling Techniques. It provides the fundamentals and is very clear. Leslie Kish's Survey Sampling is another classic that I can recommend. An advantage of going to amazon is that there are often many user generated book reviews there for the OP to look at. I personally have written a lot of revi... | Looking for a good online resource about survey design and analysis | I recommend Cochran's Sampling Techniques. It provides the fundamentals and is very clear. Leslie Kish's Survey Sampling is another classic that I can recommend. An advantage of going to amazon is th | Looking for a good online resource about survey design and analysis
I recommend Cochran's Sampling Techniques. It provides the fundamentals and is very clear. Leslie Kish's Survey Sampling is another classic that I can recommend. An advantage of going to amazon is that there are often many user generated book reviews ... | Looking for a good online resource about survey design and analysis
I recommend Cochran's Sampling Techniques. It provides the fundamentals and is very clear. Leslie Kish's Survey Sampling is another classic that I can recommend. An advantage of going to amazon is th |
35,231 | Looking for a good online resource about survey design and analysis | Previous answers seem to have well addressed OP's question. However, I will add, for the benefit of future readers, that Thomas Lumley has provided a wealth of information on "complex surveys", which can be loosely characterized as surveys with often thousands to tens of millions or more of observations (perhaps data ... | Looking for a good online resource about survey design and analysis | Previous answers seem to have well addressed OP's question. However, I will add, for the benefit of future readers, that Thomas Lumley has provided a wealth of information on "complex surveys", which | Looking for a good online resource about survey design and analysis
Previous answers seem to have well addressed OP's question. However, I will add, for the benefit of future readers, that Thomas Lumley has provided a wealth of information on "complex surveys", which can be loosely characterized as surveys with often ... | Looking for a good online resource about survey design and analysis
Previous answers seem to have well addressed OP's question. However, I will add, for the benefit of future readers, that Thomas Lumley has provided a wealth of information on "complex surveys", which |
35,232 | How to denote element-wise difference of two matrices | Assume your matrices are called $A$ and $B$, then it is usual to notate their elements with $a_{ij}$ respectively $b_{ij}$. So you could denote the sum of the squared errors as
$$
\text{SSE} = \sum_{i,j} (a_{ij}-b_{ij})^2.
$$
You would get your MSE in the usual way, by taking the average. Does this answer your questi... | How to denote element-wise difference of two matrices | Assume your matrices are called $A$ and $B$, then it is usual to notate their elements with $a_{ij}$ respectively $b_{ij}$. So you could denote the sum of the squared errors as
$$
\text{SSE} = \sum_{ | How to denote element-wise difference of two matrices
Assume your matrices are called $A$ and $B$, then it is usual to notate their elements with $a_{ij}$ respectively $b_{ij}$. So you could denote the sum of the squared errors as
$$
\text{SSE} = \sum_{i,j} (a_{ij}-b_{ij})^2.
$$
You would get your MSE in the usual wa... | How to denote element-wise difference of two matrices
Assume your matrices are called $A$ and $B$, then it is usual to notate their elements with $a_{ij}$ respectively $b_{ij}$. So you could denote the sum of the squared errors as
$$
\text{SSE} = \sum_{ |
35,233 | How to denote element-wise difference of two matrices | Standard notation for addition/subtraction of matrices refers to elementwise addition/subtraction, so with standard notation you have:
$$\mathbf{A}-\mathbf{B} = \begin{bmatrix}
a_{11} - b_{11} & a_{12} - b_{12} & \cdots & a_{1m} - b_{1m} \\
a_{21} - b_{21} & a_{22} - b_{22} & \cdots & a_{2m} - b_{2m} \\
\vdots & \vdot... | How to denote element-wise difference of two matrices | Standard notation for addition/subtraction of matrices refers to elementwise addition/subtraction, so with standard notation you have:
$$\mathbf{A}-\mathbf{B} = \begin{bmatrix}
a_{11} - b_{11} & a_{1 | How to denote element-wise difference of two matrices
Standard notation for addition/subtraction of matrices refers to elementwise addition/subtraction, so with standard notation you have:
$$\mathbf{A}-\mathbf{B} = \begin{bmatrix}
a_{11} - b_{11} & a_{12} - b_{12} & \cdots & a_{1m} - b_{1m} \\
a_{21} - b_{21} & a_{22}... | How to denote element-wise difference of two matrices
Standard notation for addition/subtraction of matrices refers to elementwise addition/subtraction, so with standard notation you have:
$$\mathbf{A}-\mathbf{B} = \begin{bmatrix}
a_{11} - b_{11} & a_{1 |
35,234 | SEM with binary dependent variable | Did you read the original Olsson (1979) paper? I believe it still provides the best description of what polychoric correlations are (although I've probably skimmed only 10% of the existing literature, I have to admit; at some point, it just gets too repetitive of the limited number of ideas though). Polychoric correlat... | SEM with binary dependent variable | Did you read the original Olsson (1979) paper? I believe it still provides the best description of what polychoric correlations are (although I've probably skimmed only 10% of the existing literature, | SEM with binary dependent variable
Did you read the original Olsson (1979) paper? I believe it still provides the best description of what polychoric correlations are (although I've probably skimmed only 10% of the existing literature, I have to admit; at some point, it just gets too repetitive of the limited number of... | SEM with binary dependent variable
Did you read the original Olsson (1979) paper? I believe it still provides the best description of what polychoric correlations are (although I've probably skimmed only 10% of the existing literature, |
35,235 | SEM with binary dependent variable | @StasK is correct in stating that the polychoric correlations can be interpreted similarly to pearson correlations, however, it sounds as if you are attempting to build a latent variable model and not simply interpret the correlation matrix, so i suggest not worry about direct interpretation. suffice to say that polych... | SEM with binary dependent variable | @StasK is correct in stating that the polychoric correlations can be interpreted similarly to pearson correlations, however, it sounds as if you are attempting to build a latent variable model and not | SEM with binary dependent variable
@StasK is correct in stating that the polychoric correlations can be interpreted similarly to pearson correlations, however, it sounds as if you are attempting to build a latent variable model and not simply interpret the correlation matrix, so i suggest not worry about direct interpr... | SEM with binary dependent variable
@StasK is correct in stating that the polychoric correlations can be interpreted similarly to pearson correlations, however, it sounds as if you are attempting to build a latent variable model and not |
35,236 | On what tasks does neuroevolution outperform basic application of neural networks or genetic algorithms? | This has been researched for 20 years or so, and there are many papers claiming to outperform backpropagation. Xin Yao did a lot of work on this in the 1990s, and Kenneth Stanley created one of the currently most active frameworks, NEAT (NeuroEvolution of Augmenting Topologies (see http://www.cs.ucf.edu/~kstanley/neat.... | On what tasks does neuroevolution outperform basic application of neural networks or genetic algorit | This has been researched for 20 years or so, and there are many papers claiming to outperform backpropagation. Xin Yao did a lot of work on this in the 1990s, and Kenneth Stanley created one of the cu | On what tasks does neuroevolution outperform basic application of neural networks or genetic algorithms?
This has been researched for 20 years or so, and there are many papers claiming to outperform backpropagation. Xin Yao did a lot of work on this in the 1990s, and Kenneth Stanley created one of the currently most ac... | On what tasks does neuroevolution outperform basic application of neural networks or genetic algorit
This has been researched for 20 years or so, and there are many papers claiming to outperform backpropagation. Xin Yao did a lot of work on this in the 1990s, and Kenneth Stanley created one of the cu |
35,237 | Smallest Kullback-Leibler divergence | If you express the Kullback–Leibler divergence when $p_2$ is a normal pdf on $\mathbb R^d$,
\begin{align}
D&(p_1||p_2) =\int_{\mathbb R^d} p_1 \log p_1 \text{d}\lambda - \int_{\mathbb R^d} p_1 \log p_2 \text{d}\lambda\\
&= \int_{\mathbb R^d} p_1 \log p_1 \text{d}\lambda - \dfrac{1}{2} \int_{\mathbb R^d} p_1 \left\{-(x-... | Smallest Kullback-Leibler divergence | If you express the Kullback–Leibler divergence when $p_2$ is a normal pdf on $\mathbb R^d$,
\begin{align}
D&(p_1||p_2) =\int_{\mathbb R^d} p_1 \log p_1 \text{d}\lambda - \int_{\mathbb R^d} p_1 \log p_ | Smallest Kullback-Leibler divergence
If you express the Kullback–Leibler divergence when $p_2$ is a normal pdf on $\mathbb R^d$,
\begin{align}
D&(p_1||p_2) =\int_{\mathbb R^d} p_1 \log p_1 \text{d}\lambda - \int_{\mathbb R^d} p_1 \log p_2 \text{d}\lambda\\
&= \int_{\mathbb R^d} p_1 \log p_1 \text{d}\lambda - \dfrac{1}{... | Smallest Kullback-Leibler divergence
If you express the Kullback–Leibler divergence when $p_2$ is a normal pdf on $\mathbb R^d$,
\begin{align}
D&(p_1||p_2) =\int_{\mathbb R^d} p_1 \log p_1 \text{d}\lambda - \int_{\mathbb R^d} p_1 \log p_ |
35,238 | What is the difference between 2x2 factorial design experiment and a 2-way ANOVA? | A 2x2 factorial design is a trial design meant to be able to more efficiently test two interventions in one sample. For instance, testing aspirin versus placebo and clonidine versus placebo in a randomized trial (the POISE-2 trial is doing this). Each patient is randomized to (clonidine or placebo) and (aspirin or plac... | What is the difference between 2x2 factorial design experiment and a 2-way ANOVA? | A 2x2 factorial design is a trial design meant to be able to more efficiently test two interventions in one sample. For instance, testing aspirin versus placebo and clonidine versus placebo in a rando | What is the difference between 2x2 factorial design experiment and a 2-way ANOVA?
A 2x2 factorial design is a trial design meant to be able to more efficiently test two interventions in one sample. For instance, testing aspirin versus placebo and clonidine versus placebo in a randomized trial (the POISE-2 trial is doin... | What is the difference between 2x2 factorial design experiment and a 2-way ANOVA?
A 2x2 factorial design is a trial design meant to be able to more efficiently test two interventions in one sample. For instance, testing aspirin versus placebo and clonidine versus placebo in a rando |
35,239 | Why the infrequent use of machine learning techniques in translational biomedicine? | Machine learning techniques often lack interpretability. Also, they tend to be rather crude from a statistical point of view --- e.g. neural networks make no assumptions about the input data. I have a feeling that lots of people (especially if they have a strong statistical background) look down on them. | Why the infrequent use of machine learning techniques in translational biomedicine? | Machine learning techniques often lack interpretability. Also, they tend to be rather crude from a statistical point of view --- e.g. neural networks make no assumptions about the input data. I have | Why the infrequent use of machine learning techniques in translational biomedicine?
Machine learning techniques often lack interpretability. Also, they tend to be rather crude from a statistical point of view --- e.g. neural networks make no assumptions about the input data. I have a feeling that lots of people (espe... | Why the infrequent use of machine learning techniques in translational biomedicine?
Machine learning techniques often lack interpretability. Also, they tend to be rather crude from a statistical point of view --- e.g. neural networks make no assumptions about the input data. I have |
35,240 | Why the infrequent use of machine learning techniques in translational biomedicine? | The track record of machine learning in biomedicine has not been very good. Early successes in machine learning came in high signal:noise ratio pattern recognition areas such as visual pattern recognition. The S:N ratio is much lower in biology and the social sciences. Machine learning effectively fits a lot of inte... | Why the infrequent use of machine learning techniques in translational biomedicine? | The track record of machine learning in biomedicine has not been very good. Early successes in machine learning came in high signal:noise ratio pattern recognition areas such as visual pattern recogn | Why the infrequent use of machine learning techniques in translational biomedicine?
The track record of machine learning in biomedicine has not been very good. Early successes in machine learning came in high signal:noise ratio pattern recognition areas such as visual pattern recognition. The S:N ratio is much lower ... | Why the infrequent use of machine learning techniques in translational biomedicine?
The track record of machine learning in biomedicine has not been very good. Early successes in machine learning came in high signal:noise ratio pattern recognition areas such as visual pattern recogn |
35,241 | Visualize sets and their connections | Let $C$ by a table and $C[i,j]$ is number of users that use both $i$-th and $j$-th feature. $C[i,i]$ is number of users using $i$-th feature. By $N$ we denote the total number of users.
One possibility is to plot the table, and (below there are some suggestions):
only its left triangular part (as $C[i,j]=C[j,i]$),
wit... | Visualize sets and their connections | Let $C$ by a table and $C[i,j]$ is number of users that use both $i$-th and $j$-th feature. $C[i,i]$ is number of users using $i$-th feature. By $N$ we denote the total number of users.
One possibilit | Visualize sets and their connections
Let $C$ by a table and $C[i,j]$ is number of users that use both $i$-th and $j$-th feature. $C[i,i]$ is number of users using $i$-th feature. By $N$ we denote the total number of users.
One possibility is to plot the table, and (below there are some suggestions):
only its left tria... | Visualize sets and their connections
Let $C$ by a table and $C[i,j]$ is number of users that use both $i$-th and $j$-th feature. $C[i,i]$ is number of users using $i$-th feature. By $N$ we denote the total number of users.
One possibilit |
35,242 | Visualize sets and their connections | I think you may be interested in circular displays for tabular data (in your case, a two-way table denoting the co-occurence of every binary features), as proposed through Circos; see example and on-line demo here.
Sidenote: As an alternative, you can also take a look at Parallel Sets that were developed by Robert Kosa... | Visualize sets and their connections | I think you may be interested in circular displays for tabular data (in your case, a two-way table denoting the co-occurence of every binary features), as proposed through Circos; see example and on-l | Visualize sets and their connections
I think you may be interested in circular displays for tabular data (in your case, a two-way table denoting the co-occurence of every binary features), as proposed through Circos; see example and on-line demo here.
Sidenote: As an alternative, you can also take a look at Parallel Se... | Visualize sets and their connections
I think you may be interested in circular displays for tabular data (in your case, a two-way table denoting the co-occurence of every binary features), as proposed through Circos; see example and on-l |
35,243 | Visualize sets and their connections | The simple approach for #2 would be a cross-tabulation: the list of 10 features across the top and down the side, with the intersecting usage of each feature shown as a count, or as various percentages. Percentages are incredibly flexible: you can base the percentages to column count, or table count, and those counts c... | Visualize sets and their connections | The simple approach for #2 would be a cross-tabulation: the list of 10 features across the top and down the side, with the intersecting usage of each feature shown as a count, or as various percentage | Visualize sets and their connections
The simple approach for #2 would be a cross-tabulation: the list of 10 features across the top and down the side, with the intersecting usage of each feature shown as a count, or as various percentages. Percentages are incredibly flexible: you can base the percentages to column coun... | Visualize sets and their connections
The simple approach for #2 would be a cross-tabulation: the list of 10 features across the top and down the side, with the intersecting usage of each feature shown as a count, or as various percentage |
35,244 | Visualize sets and their connections | A couple of simple ideas:
bar graph of pairs (or sets) of features
a separate bar graph, for each feature, of how many times each other feature appears with it. | Visualize sets and their connections | A couple of simple ideas:
bar graph of pairs (or sets) of features
a separate bar graph, for each feature, of how many times each other feature appears with it. | Visualize sets and their connections
A couple of simple ideas:
bar graph of pairs (or sets) of features
a separate bar graph, for each feature, of how many times each other feature appears with it. | Visualize sets and their connections
A couple of simple ideas:
bar graph of pairs (or sets) of features
a separate bar graph, for each feature, of how many times each other feature appears with it. |
35,245 | Why do people often run a regression with and without control variables? | A little bit on terms first. By definition control variable is kept constant through the study, so you can't use it in regression. You probably mean variables that should be statistically controlled for. Such as covariates or blocking factors (as after randomized block experimental design)
People run regression or ANOV... | Why do people often run a regression with and without control variables? | A little bit on terms first. By definition control variable is kept constant through the study, so you can't use it in regression. You probably mean variables that should be statistically controlled f | Why do people often run a regression with and without control variables?
A little bit on terms first. By definition control variable is kept constant through the study, so you can't use it in regression. You probably mean variables that should be statistically controlled for. Such as covariates or blocking factors (as ... | Why do people often run a regression with and without control variables?
A little bit on terms first. By definition control variable is kept constant through the study, so you can't use it in regression. You probably mean variables that should be statistically controlled f |
35,246 | Why do people often run a regression with and without control variables? | One more reason to include covariates is that they are important in the literature. If you can demonstrate that some covariate that has been found to have large effects in the past (either on its own or by affecting other parameters) does NOT have large effects in your study, then you have discovered something interes... | Why do people often run a regression with and without control variables? | One more reason to include covariates is that they are important in the literature. If you can demonstrate that some covariate that has been found to have large effects in the past (either on its own | Why do people often run a regression with and without control variables?
One more reason to include covariates is that they are important in the literature. If you can demonstrate that some covariate that has been found to have large effects in the past (either on its own or by affecting other parameters) does NOT hav... | Why do people often run a regression with and without control variables?
One more reason to include covariates is that they are important in the literature. If you can demonstrate that some covariate that has been found to have large effects in the past (either on its own |
35,247 | Why do people often run a regression with and without control variables? | Typically, this means that there is a regression with an outcome and a treatment variable. Then, there are other controls that could be added to the model---other covariates that may be important. The authors first run a simple model that only includes treatment. Then, they check the robustness of their findings to the... | Why do people often run a regression with and without control variables? | Typically, this means that there is a regression with an outcome and a treatment variable. Then, there are other controls that could be added to the model---other covariates that may be important. The | Why do people often run a regression with and without control variables?
Typically, this means that there is a regression with an outcome and a treatment variable. Then, there are other controls that could be added to the model---other covariates that may be important. The authors first run a simple model that only inc... | Why do people often run a regression with and without control variables?
Typically, this means that there is a regression with an outcome and a treatment variable. Then, there are other controls that could be added to the model---other covariates that may be important. The |
35,248 | Why do people often run a regression with and without control variables? | In addition to the answers above, there are some covariate selection techniques that involve comparing models with and without a variable in place. And if one wishes to illustrate the effect of adding a covariate, the crude (unadjusted) model is necessary as a reference in the first place. | Why do people often run a regression with and without control variables? | In addition to the answers above, there are some covariate selection techniques that involve comparing models with and without a variable in place. And if one wishes to illustrate the effect of adding | Why do people often run a regression with and without control variables?
In addition to the answers above, there are some covariate selection techniques that involve comparing models with and without a variable in place. And if one wishes to illustrate the effect of adding a covariate, the crude (unadjusted) model is n... | Why do people often run a regression with and without control variables?
In addition to the answers above, there are some covariate selection techniques that involve comparing models with and without a variable in place. And if one wishes to illustrate the effect of adding |
35,249 | The exact distribution of Wilcoxon rank-sum statistic U | AFAIK, there is no closed form for the distribution. Using R, the naive implementation of getting the exact distribution works for me up to group sizes of at least 12 - that takes less than 1 minute on a Core i5 using Windows7 64bit and current R. For R's own more clever algorithm in C that's used in pwilcox(), you can... | The exact distribution of Wilcoxon rank-sum statistic U | AFAIK, there is no closed form for the distribution. Using R, the naive implementation of getting the exact distribution works for me up to group sizes of at least 12 - that takes less than 1 minute o | The exact distribution of Wilcoxon rank-sum statistic U
AFAIK, there is no closed form for the distribution. Using R, the naive implementation of getting the exact distribution works for me up to group sizes of at least 12 - that takes less than 1 minute on a Core i5 using Windows7 64bit and current R. For R's own more... | The exact distribution of Wilcoxon rank-sum statistic U
AFAIK, there is no closed form for the distribution. Using R, the naive implementation of getting the exact distribution works for me up to group sizes of at least 12 - that takes less than 1 minute o |
35,250 | The exact distribution of Wilcoxon rank-sum statistic U | Caracal's answer is nice, but it's important to consider that the large sample approximation works best for equal sample sizes, and can perform considerably worse for unbalanced samples.
The paper you (and I) are looking for is for more general statistics than the Wilcoxon (Jonckheere-Terpstra, Umbrella tests, etc...)... | The exact distribution of Wilcoxon rank-sum statistic U | Caracal's answer is nice, but it's important to consider that the large sample approximation works best for equal sample sizes, and can perform considerably worse for unbalanced samples.
The paper yo | The exact distribution of Wilcoxon rank-sum statistic U
Caracal's answer is nice, but it's important to consider that the large sample approximation works best for equal sample sizes, and can perform considerably worse for unbalanced samples.
The paper you (and I) are looking for is for more general statistics than th... | The exact distribution of Wilcoxon rank-sum statistic U
Caracal's answer is nice, but it's important to consider that the large sample approximation works best for equal sample sizes, and can perform considerably worse for unbalanced samples.
The paper yo |
35,251 | Can a 2-2-1 feedforward neural network with sigmoid activation functions represent a solution to XOR? | It is not possible to give a perfect logical gate with a logistic output neuron because the range is $(0,1)$, but you can approximate $(0,0)\mapsto 0, (0,1)\mapsto 1, (1,0) \mapsto 1, (1,1) \mapsto 0$ arbitrarily well, contrary to the previous answer.
Let the inputs be $i,j$. Let the first hidden neuron have weights $(... | Can a 2-2-1 feedforward neural network with sigmoid activation functions represent a solution to XOR | It is not possible to give a perfect logical gate with a logistic output neuron because the range is $(0,1)$, but you can approximate $(0,0)\mapsto 0, (0,1)\mapsto 1, (1,0) \mapsto 1, (1,1) \mapsto 0$ | Can a 2-2-1 feedforward neural network with sigmoid activation functions represent a solution to XOR?
It is not possible to give a perfect logical gate with a logistic output neuron because the range is $(0,1)$, but you can approximate $(0,0)\mapsto 0, (0,1)\mapsto 1, (1,0) \mapsto 1, (1,1) \mapsto 0$ arbitrarily well,... | Can a 2-2-1 feedforward neural network with sigmoid activation functions represent a solution to XOR
It is not possible to give a perfect logical gate with a logistic output neuron because the range is $(0,1)$, but you can approximate $(0,0)\mapsto 0, (0,1)\mapsto 1, (1,0) \mapsto 1, (1,1) \mapsto 0$ |
35,252 | Linear regression terminology question -- Beta (β) | You're right. Most texts I've seen write a regression model as $$Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_{p-1} X_{p-1} + \epsilon,$$ and the second usage of "beta" or "beta weight" to mean "standardised regession coefficient" is also relatively common (and is used in some statistical software).
I avoid this ambiguit... | Linear regression terminology question -- Beta (β) | You're right. Most texts I've seen write a regression model as $$Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_{p-1} X_{p-1} + \epsilon,$$ and the second usage of "beta" or "beta weight" to mean "standar | Linear regression terminology question -- Beta (β)
You're right. Most texts I've seen write a regression model as $$Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_{p-1} X_{p-1} + \epsilon,$$ and the second usage of "beta" or "beta weight" to mean "standardised regession coefficient" is also relatively common (and is used i... | Linear regression terminology question -- Beta (β)
You're right. Most texts I've seen write a regression model as $$Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_{p-1} X_{p-1} + \epsilon,$$ and the second usage of "beta" or "beta weight" to mean "standar |
35,253 | Linear regression terminology question -- Beta (β) | Assuming the linear model is correct, $\hat{\beta}$ (this is sometimes also called $b$ in elementary texts), the coefficient estimated from the data set you have, is an estimate of the true slope, $\beta$. To my knowledge there is no standard notation for the standardized coefficient, although some simple algebra will ... | Linear regression terminology question -- Beta (β) | Assuming the linear model is correct, $\hat{\beta}$ (this is sometimes also called $b$ in elementary texts), the coefficient estimated from the data set you have, is an estimate of the true slope, $\b | Linear regression terminology question -- Beta (β)
Assuming the linear model is correct, $\hat{\beta}$ (this is sometimes also called $b$ in elementary texts), the coefficient estimated from the data set you have, is an estimate of the true slope, $\beta$. To my knowledge there is no standard notation for the standardi... | Linear regression terminology question -- Beta (β)
Assuming the linear model is correct, $\hat{\beta}$ (this is sometimes also called $b$ in elementary texts), the coefficient estimated from the data set you have, is an estimate of the true slope, $\b |
35,254 | Generating random samples from a density function | Denote the joint density you want to sample from by $p_{XY}(x,y)$. Then the marginal densities are easily obtainable:
$$ p_{X}(x) = \int_{D_{Y}} p_{XY}(x,y) dy, \ \ \ \ \ \ \ \ \ p_{Y}(y) = \int_{D_{X}} p_{XY}(x,y) dx $$
where $D_X, D_Y$ denote the support of $X, Y$, respectively. From this you can calculate each of t... | Generating random samples from a density function | Denote the joint density you want to sample from by $p_{XY}(x,y)$. Then the marginal densities are easily obtainable:
$$ p_{X}(x) = \int_{D_{Y}} p_{XY}(x,y) dy, \ \ \ \ \ \ \ \ \ p_{Y}(y) = \int_{D_{ | Generating random samples from a density function
Denote the joint density you want to sample from by $p_{XY}(x,y)$. Then the marginal densities are easily obtainable:
$$ p_{X}(x) = \int_{D_{Y}} p_{XY}(x,y) dy, \ \ \ \ \ \ \ \ \ p_{Y}(y) = \int_{D_{X}} p_{XY}(x,y) dx $$
where $D_X, D_Y$ denote the support of $X, Y$, r... | Generating random samples from a density function
Denote the joint density you want to sample from by $p_{XY}(x,y)$. Then the marginal densities are easily obtainable:
$$ p_{X}(x) = \int_{D_{Y}} p_{XY}(x,y) dy, \ \ \ \ \ \ \ \ \ p_{Y}(y) = \int_{D_{ |
35,255 | Generating random samples from a density function | You can use rejection sampling.
Let $f$ be the probability density we want to sample from
(it does not matter if it is 1- or 2-dimensional).
If we can find a probability distribution $g$,
from which we can easily sample from,
e.g., a uniform distribution (a good choice, if the support of $f$ is bounded),
a Gaussian d... | Generating random samples from a density function | You can use rejection sampling.
Let $f$ be the probability density we want to sample from
(it does not matter if it is 1- or 2-dimensional).
If we can find a probability distribution $g$,
from which | Generating random samples from a density function
You can use rejection sampling.
Let $f$ be the probability density we want to sample from
(it does not matter if it is 1- or 2-dimensional).
If we can find a probability distribution $g$,
from which we can easily sample from,
e.g., a uniform distribution (a good choic... | Generating random samples from a density function
You can use rejection sampling.
Let $f$ be the probability density we want to sample from
(it does not matter if it is 1- or 2-dimensional).
If we can find a probability distribution $g$,
from which |
35,256 | What is covariance matrix adaptation evolution strategy? | Per the wikipedia page linked above to answer (1) this is another form of gradient descent (which if you need more information with lots of pictures there are many articles available if you google it -- sorry apparently new posters only get 2 urls so I'm having to have to tell you to search instead of pointing you to s... | What is covariance matrix adaptation evolution strategy? | Per the wikipedia page linked above to answer (1) this is another form of gradient descent (which if you need more information with lots of pictures there are many articles available if you google it | What is covariance matrix adaptation evolution strategy?
Per the wikipedia page linked above to answer (1) this is another form of gradient descent (which if you need more information with lots of pictures there are many articles available if you google it -- sorry apparently new posters only get 2 urls so I'm having t... | What is covariance matrix adaptation evolution strategy?
Per the wikipedia page linked above to answer (1) this is another form of gradient descent (which if you need more information with lots of pictures there are many articles available if you google it |
35,257 | What is covariance matrix adaptation evolution strategy? | It's an optimization algorithm: it tries to find the minimum of a function. It is said to be amongst the best optimization algorithms for non-convex problems in high dimensions (above 5 or 10 parameters to optimize).
The term Covariance in the name is a bit misleading to the statistics community: there is no statistics... | What is covariance matrix adaptation evolution strategy? | It's an optimization algorithm: it tries to find the minimum of a function. It is said to be amongst the best optimization algorithms for non-convex problems in high dimensions (above 5 or 10 paramete | What is covariance matrix adaptation evolution strategy?
It's an optimization algorithm: it tries to find the minimum of a function. It is said to be amongst the best optimization algorithms for non-convex problems in high dimensions (above 5 or 10 parameters to optimize).
The term Covariance in the name is a bit misle... | What is covariance matrix adaptation evolution strategy?
It's an optimization algorithm: it tries to find the minimum of a function. It is said to be amongst the best optimization algorithms for non-convex problems in high dimensions (above 5 or 10 paramete |
35,258 | Quality-price trade-off | The Keeney-Raiffa approach to Multi-attribute valuation theory is well-grounded practically and theoretically, has been successfully applied to many problems, and--when applied to problems with just two attributes--is particularly simple. It proceeds by systematically exploring the trade-offs you would actually make i... | Quality-price trade-off | The Keeney-Raiffa approach to Multi-attribute valuation theory is well-grounded practically and theoretically, has been successfully applied to many problems, and--when applied to problems with just t | Quality-price trade-off
The Keeney-Raiffa approach to Multi-attribute valuation theory is well-grounded practically and theoretically, has been successfully applied to many problems, and--when applied to problems with just two attributes--is particularly simple. It proceeds by systematically exploring the trade-offs ... | Quality-price trade-off
The Keeney-Raiffa approach to Multi-attribute valuation theory is well-grounded practically and theoretically, has been successfully applied to many problems, and--when applied to problems with just t |
35,259 | How to compare Likert scales with varying number of categories across time? | Apply empirically-based rescaling formula: If you can administer both versions of the scale to a subsample, you could estimate what the corresponding scores are on the two response formats. Then you could apply a conversion formula that is empirically justified.
There are several ways that you could do this. For instan... | How to compare Likert scales with varying number of categories across time? | Apply empirically-based rescaling formula: If you can administer both versions of the scale to a subsample, you could estimate what the corresponding scores are on the two response formats. Then you c | How to compare Likert scales with varying number of categories across time?
Apply empirically-based rescaling formula: If you can administer both versions of the scale to a subsample, you could estimate what the corresponding scores are on the two response formats. Then you could apply a conversion formula that is empi... | How to compare Likert scales with varying number of categories across time?
Apply empirically-based rescaling formula: If you can administer both versions of the scale to a subsample, you could estimate what the corresponding scores are on the two response formats. Then you c |
35,260 | How to compare Likert scales with varying number of categories across time? | [Technically you've got survey items, not Likert scales; the latter are fashioned from multiple items. See, for example, Paul Spector's Summated Rating Scale Construction {Sage}.]
The steps you take will need to depend on the audience for which you're reporting. If it's academic and rigorous, like a dissertation co... | How to compare Likert scales with varying number of categories across time? | [Technically you've got survey items, not Likert scales; the latter are fashioned from multiple items. See, for example, Paul Spector's Summated Rating Scale Construction {Sage}.]
The steps you tak | How to compare Likert scales with varying number of categories across time?
[Technically you've got survey items, not Likert scales; the latter are fashioned from multiple items. See, for example, Paul Spector's Summated Rating Scale Construction {Sage}.]
The steps you take will need to depend on the audience for wh... | How to compare Likert scales with varying number of categories across time?
[Technically you've got survey items, not Likert scales; the latter are fashioned from multiple items. See, for example, Paul Spector's Summated Rating Scale Construction {Sage}.]
The steps you tak |
35,261 | How to compare Likert scales with varying number of categories across time? | Consider transforming the responses from both data sets into z-scores. There is going to be an ad hoc quality to any sort of rescaling but at least this way you avoid mechanically treating any particular set of intervals on one item as equivalent to any particular set on the other. I'd definitely go this route if I wer... | How to compare Likert scales with varying number of categories across time? | Consider transforming the responses from both data sets into z-scores. There is going to be an ad hoc quality to any sort of rescaling but at least this way you avoid mechanically treating any particu | How to compare Likert scales with varying number of categories across time?
Consider transforming the responses from both data sets into z-scores. There is going to be an ad hoc quality to any sort of rescaling but at least this way you avoid mechanically treating any particular set of intervals on one item as equivale... | How to compare Likert scales with varying number of categories across time?
Consider transforming the responses from both data sets into z-scores. There is going to be an ad hoc quality to any sort of rescaling but at least this way you avoid mechanically treating any particu |
35,262 | How to compare Likert scales with varying number of categories across time? | I've just had to solve this exact problem. We had a 9 point scale that was changed to a 5 point scale on a tracker going back 10 years. Not only that but some of the statements changed as well. And we were reporting as a form of Net Promoter Score.
The solution we used apply's a paired design by asking each respondent ... | How to compare Likert scales with varying number of categories across time? | I've just had to solve this exact problem. We had a 9 point scale that was changed to a 5 point scale on a tracker going back 10 years. Not only that but some of the statements changed as well. And we | How to compare Likert scales with varying number of categories across time?
I've just had to solve this exact problem. We had a 9 point scale that was changed to a 5 point scale on a tracker going back 10 years. Not only that but some of the statements changed as well. And we were reporting as a form of Net Promoter Sc... | How to compare Likert scales with varying number of categories across time?
I've just had to solve this exact problem. We had a 9 point scale that was changed to a 5 point scale on a tracker going back 10 years. Not only that but some of the statements changed as well. And we |
35,263 | Group vs Stacked Bar Plots | I think grouped bars are preferable to stacked bars in most situations because they retain information about the sizes of the groups and stay readable even when you have multiple nominal categories. For me, the segments of stacked bars get difficult to compare beyond two categories - and even with just two categories,... | Group vs Stacked Bar Plots | I think grouped bars are preferable to stacked bars in most situations because they retain information about the sizes of the groups and stay readable even when you have multiple nominal categories. | Group vs Stacked Bar Plots
I think grouped bars are preferable to stacked bars in most situations because they retain information about the sizes of the groups and stay readable even when you have multiple nominal categories. For me, the segments of stacked bars get difficult to compare beyond two categories - and eve... | Group vs Stacked Bar Plots
I think grouped bars are preferable to stacked bars in most situations because they retain information about the sizes of the groups and stay readable even when you have multiple nominal categories. |
35,264 | Group vs Stacked Bar Plots | Stacked barcharts useless? Here's an appropriate use of one, actually, a hybrid, grouped and stacked bar chart. Lets say I set 1 milliliter of milk aside while passing a 2nd milliliter through a column packed with a resin capable of adsorbing a certain milk component that I'm able to later measure. Let's say I capture ... | Group vs Stacked Bar Plots | Stacked barcharts useless? Here's an appropriate use of one, actually, a hybrid, grouped and stacked bar chart. Lets say I set 1 milliliter of milk aside while passing a 2nd milliliter through a colum | Group vs Stacked Bar Plots
Stacked barcharts useless? Here's an appropriate use of one, actually, a hybrid, grouped and stacked bar chart. Lets say I set 1 milliliter of milk aside while passing a 2nd milliliter through a column packed with a resin capable of adsorbing a certain milk component that I'm able to later me... | Group vs Stacked Bar Plots
Stacked barcharts useless? Here's an appropriate use of one, actually, a hybrid, grouped and stacked bar chart. Lets say I set 1 milliliter of milk aside while passing a 2nd milliliter through a colum |
35,265 | Group vs Stacked Bar Plots | I don't think there are any appropriate uses of stacked bar charts; grouped bar charts are better, but both are inferior to other plots, depending on what aspect of your data you want to emphasize, and how much data you have. | Group vs Stacked Bar Plots | I don't think there are any appropriate uses of stacked bar charts; grouped bar charts are better, but both are inferior to other plots, depending on what aspect of your data you want to emphasize, an | Group vs Stacked Bar Plots
I don't think there are any appropriate uses of stacked bar charts; grouped bar charts are better, but both are inferior to other plots, depending on what aspect of your data you want to emphasize, and how much data you have. | Group vs Stacked Bar Plots
I don't think there are any appropriate uses of stacked bar charts; grouped bar charts are better, but both are inferior to other plots, depending on what aspect of your data you want to emphasize, an |
35,266 | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficients consistent estimators? | If you can assume that your rows of your data matrix are exchangeable then your modelling strategy should work well. Your method should be fine under the conditions stated by Gaetan Lion before.
The reason why your method will work (given the exchangeability assumption holds) is that it be taken as a special case of p... | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficient | If you can assume that your rows of your data matrix are exchangeable then your modelling strategy should work well. Your method should be fine under the conditions stated by Gaetan Lion before.
The | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficients consistent estimators?
If you can assume that your rows of your data matrix are exchangeable then your modelling strategy should work well. Your method should be fine under the conditions stated by Gaetan Lion before. ... | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficient
If you can assume that your rows of your data matrix are exchangeable then your modelling strategy should work well. Your method should be fine under the conditions stated by Gaetan Lion before.
The |
35,267 | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficients consistent estimators? | The answer to your original question is yes, because the classical theory applies under your sampling scheme. You don’t need any assumptions on the original data matrix. All of the randomness (implicitly behind standard errors and consistency) comes from your scheme for sampling $N$ rows from the data matrix.
Think o... | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficient | The answer to your original question is yes, because the classical theory applies under your sampling scheme. You don’t need any assumptions on the original data matrix. All of the randomness (impli | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficients consistent estimators?
The answer to your original question is yes, because the classical theory applies under your sampling scheme. You don’t need any assumptions on the original data matrix. All of the randomness (... | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficient
The answer to your original question is yes, because the classical theory applies under your sampling scheme. You don’t need any assumptions on the original data matrix. All of the randomness (impli |
35,268 | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficients consistent estimators? | The greater the sample N, the smaller the standard error (higher t stat, and smaller the respective p values) associated with all your regression coefficients. The greater M, the more datapoints you will have and the smaller will be your standard error of the mean of the coefficients over M runs. Such means should ha... | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficient | The greater the sample N, the smaller the standard error (higher t stat, and smaller the respective p values) associated with all your regression coefficients. The greater M, the more datapoints you | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficients consistent estimators?
The greater the sample N, the smaller the standard error (higher t stat, and smaller the respective p values) associated with all your regression coefficients. The greater M, the more datapoints... | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficient
The greater the sample N, the smaller the standard error (higher t stat, and smaller the respective p values) associated with all your regression coefficients. The greater M, the more datapoints you |
35,269 | Obtaining SAS experience | I would recommend going through a self-study course such as the UCLA website and specifically the SAS Starter Kit. If you learn better within an interactive environment, I would suggest checking out online course offerings such as the World Campus SAS courses offered at Penn State University (Stat 480, 481, & 482).
Up... | Obtaining SAS experience | I would recommend going through a self-study course such as the UCLA website and specifically the SAS Starter Kit. If you learn better within an interactive environment, I would suggest checking out | Obtaining SAS experience
I would recommend going through a self-study course such as the UCLA website and specifically the SAS Starter Kit. If you learn better within an interactive environment, I would suggest checking out online course offerings such as the World Campus SAS courses offered at Penn State University (... | Obtaining SAS experience
I would recommend going through a self-study course such as the UCLA website and specifically the SAS Starter Kit. If you learn better within an interactive environment, I would suggest checking out |
35,270 | Obtaining SAS experience | As far as SAS goes, getting certified is resume gold. The SAS Institute offers classes and exams to receive the certification. There are also books you can use if you are self-motivated.
Getting SAS is quite difficult if your company does not have it. I'm on a college campus, and they offer academic discounts on studen... | Obtaining SAS experience | As far as SAS goes, getting certified is resume gold. The SAS Institute offers classes and exams to receive the certification. There are also books you can use if you are self-motivated.
Getting SAS i | Obtaining SAS experience
As far as SAS goes, getting certified is resume gold. The SAS Institute offers classes and exams to receive the certification. There are also books you can use if you are self-motivated.
Getting SAS is quite difficult if your company does not have it. I'm on a college campus, and they offer aca... | Obtaining SAS experience
As far as SAS goes, getting certified is resume gold. The SAS Institute offers classes and exams to receive the certification. There are also books you can use if you are self-motivated.
Getting SAS i |
35,271 | Obtaining SAS experience | The programming language most similar to SAS is... SAS. Which you can interpret using WPS, which will run SAS code and evidently costs substantially less than a SAS license and has a 30 day free trial. I haven't used it myself, but it should get you started programming in the SAS language.
As M. Tibbits suggests, I d... | Obtaining SAS experience | The programming language most similar to SAS is... SAS. Which you can interpret using WPS, which will run SAS code and evidently costs substantially less than a SAS license and has a 30 day free tria | Obtaining SAS experience
The programming language most similar to SAS is... SAS. Which you can interpret using WPS, which will run SAS code and evidently costs substantially less than a SAS license and has a 30 day free trial. I haven't used it myself, but it should get you started programming in the SAS language.
As... | Obtaining SAS experience
The programming language most similar to SAS is... SAS. Which you can interpret using WPS, which will run SAS code and evidently costs substantially less than a SAS license and has a 30 day free tria |
35,272 | Obtaining SAS experience | when i joined analytics industry( just out of my own interest) after serving software for 5 yrs..I didnt know SAS either..I got some version from somewhere and started writing codes on my own. Yes, I had programming background before that..I knew SQL, I knew general programming. I would suggest you visit tutorials and ... | Obtaining SAS experience | when i joined analytics industry( just out of my own interest) after serving software for 5 yrs..I didnt know SAS either..I got some version from somewhere and started writing codes on my own. Yes, I | Obtaining SAS experience
when i joined analytics industry( just out of my own interest) after serving software for 5 yrs..I didnt know SAS either..I got some version from somewhere and started writing codes on my own. Yes, I had programming background before that..I knew SQL, I knew general programming. I would suggest... | Obtaining SAS experience
when i joined analytics industry( just out of my own interest) after serving software for 5 yrs..I didnt know SAS either..I got some version from somewhere and started writing codes on my own. Yes, I |
35,273 | Obtaining SAS experience | SAS University Edition is a great place to start! | Obtaining SAS experience | SAS University Edition is a great place to start! | Obtaining SAS experience
SAS University Edition is a great place to start! | Obtaining SAS experience
SAS University Edition is a great place to start! |
35,274 | Clustering genes in a time course experiment | It seems you just want to make a fair standard analysis, so I am not a best person to answer your question; yet I would suggest you to dive deeper into Bioconductor; it has a lot of useful stuff, nevertheless finding what you want is painful. For instance Mfuzz package looks promising. | Clustering genes in a time course experiment | It seems you just want to make a fair standard analysis, so I am not a best person to answer your question; yet I would suggest you to dive deeper into Bioconductor; it has a lot of useful stuff, neve | Clustering genes in a time course experiment
It seems you just want to make a fair standard analysis, so I am not a best person to answer your question; yet I would suggest you to dive deeper into Bioconductor; it has a lot of useful stuff, nevertheless finding what you want is painful. For instance Mfuzz package looks... | Clustering genes in a time course experiment
It seems you just want to make a fair standard analysis, so I am not a best person to answer your question; yet I would suggest you to dive deeper into Bioconductor; it has a lot of useful stuff, neve |
35,275 | Clustering genes in a time course experiment | In complement to @mbq's response (Mfuzz looks fine), I'll just put some references (PDFs) about clustering of time-course gene expression data:
Futschik, ME and Charlisle, B (2005). Noise robust clustering of gene expression time-course data. Journal of Bioinformatics and Computational Biology, 3(4), 965-988.
Luan, Y ... | Clustering genes in a time course experiment | In complement to @mbq's response (Mfuzz looks fine), I'll just put some references (PDFs) about clustering of time-course gene expression data:
Futschik, ME and Charlisle, B (2005). Noise robust clus | Clustering genes in a time course experiment
In complement to @mbq's response (Mfuzz looks fine), I'll just put some references (PDFs) about clustering of time-course gene expression data:
Futschik, ME and Charlisle, B (2005). Noise robust clustering of gene expression time-course data. Journal of Bioinformatics and C... | Clustering genes in a time course experiment
In complement to @mbq's response (Mfuzz looks fine), I'll just put some references (PDFs) about clustering of time-course gene expression data:
Futschik, ME and Charlisle, B (2005). Noise robust clus |
35,276 | Clustering genes in a time course experiment | Just to add to the other answers (which look like they should solve your problem), did you try using standard clustering algorithms for you data when constructing your dendrogram? For example,
heatmap.2(dataset, <standard args>,
hclustfun = function(c){hclust(c, method= 'average')}
)
Instead of us... | Clustering genes in a time course experiment | Just to add to the other answers (which look like they should solve your problem), did you try using standard clustering algorithms for you data when constructing your dendrogram? For example,
heatma | Clustering genes in a time course experiment
Just to add to the other answers (which look like they should solve your problem), did you try using standard clustering algorithms for you data when constructing your dendrogram? For example,
heatmap.2(dataset, <standard args>,
hclustfun = function(c){hclust(c, m... | Clustering genes in a time course experiment
Just to add to the other answers (which look like they should solve your problem), did you try using standard clustering algorithms for you data when constructing your dendrogram? For example,
heatma |
35,277 | How could I predict the results of a simple card game? | The easiest way is just to simulate the game lots of times. The R code below simulates a single game.
nplayers = 4
#Create an empty data frame to keep track
#of card number, suit and if it's magic
empty.hand = data.frame(number = numeric(52),
suit = numeric(52),
magic = numeric(52))
#A list of players who are in ... | How could I predict the results of a simple card game? | The easiest way is just to simulate the game lots of times. The R code below simulates a single game.
nplayers = 4
#Create an empty data frame to keep track
#of card number, suit and if it's magic
emp | How could I predict the results of a simple card game?
The easiest way is just to simulate the game lots of times. The R code below simulates a single game.
nplayers = 4
#Create an empty data frame to keep track
#of card number, suit and if it's magic
empty.hand = data.frame(number = numeric(52),
suit = numeric(52),
... | How could I predict the results of a simple card game?
The easiest way is just to simulate the game lots of times. The R code below simulates a single game.
nplayers = 4
#Create an empty data frame to keep track
#of card number, suit and if it's magic
emp |
35,278 | Least squares problem penalising the number of non-zero coefficients | If ridge regression is considered to penalize the usual (square) loss according to the $L_2$ norm of the parameter vector and LASSO regression is considered to penalize the loss according to the $L_1$ norm of the parameter vector, then this would be a penalty according to the $L_0$ "norm" of the parameter vector, which... | Least squares problem penalising the number of non-zero coefficients | If ridge regression is considered to penalize the usual (square) loss according to the $L_2$ norm of the parameter vector and LASSO regression is considered to penalize the loss according to the $L_1$ | Least squares problem penalising the number of non-zero coefficients
If ridge regression is considered to penalize the usual (square) loss according to the $L_2$ norm of the parameter vector and LASSO regression is considered to penalize the loss according to the $L_1$ norm of the parameter vector, then this would be a... | Least squares problem penalising the number of non-zero coefficients
If ridge regression is considered to penalize the usual (square) loss according to the $L_2$ norm of the parameter vector and LASSO regression is considered to penalize the loss according to the $L_1$ |
35,279 | Generating random variable which has a power distribution of Box and Tiao (1962) | Box & Tiao refer to this as a "convenient class of power distributions," referencing Diananda (1949), Box (1953), and Turner (1960).
Because $\mu$ and $\sigma$ just establish a unit of measurement and the absolute value reflects values around the origin, the basic density is proportional to $\exp(-z^p/2)$ where $p = 2/... | Generating random variable which has a power distribution of Box and Tiao (1962) | Box & Tiao refer to this as a "convenient class of power distributions," referencing Diananda (1949), Box (1953), and Turner (1960).
Because $\mu$ and $\sigma$ just establish a unit of measurement and | Generating random variable which has a power distribution of Box and Tiao (1962)
Box & Tiao refer to this as a "convenient class of power distributions," referencing Diananda (1949), Box (1953), and Turner (1960).
Because $\mu$ and $\sigma$ just establish a unit of measurement and the absolute value reflects values aro... | Generating random variable which has a power distribution of Box and Tiao (1962)
Box & Tiao refer to this as a "convenient class of power distributions," referencing Diananda (1949), Box (1953), and Turner (1960).
Because $\mu$ and $\sigma$ just establish a unit of measurement and |
35,280 | Textbook recommendations covering machine learning techniques for causal inference? | I follow this area pretty closely, but I think this subfield is so new no textbook exists (yet).
However, there are some course videos that are fairly good:
Machine Learning & Causal Inference: A Short Course at Stanford (accompanying tutorial)
Summer Institute in Machine Learning in Economics (MLESI21) at University ... | Textbook recommendations covering machine learning techniques for causal inference? | I follow this area pretty closely, but I think this subfield is so new no textbook exists (yet).
However, there are some course videos that are fairly good:
Machine Learning & Causal Inference: A Sho | Textbook recommendations covering machine learning techniques for causal inference?
I follow this area pretty closely, but I think this subfield is so new no textbook exists (yet).
However, there are some course videos that are fairly good:
Machine Learning & Causal Inference: A Short Course at Stanford (accompanying ... | Textbook recommendations covering machine learning techniques for causal inference?
I follow this area pretty closely, but I think this subfield is so new no textbook exists (yet).
However, there are some course videos that are fairly good:
Machine Learning & Causal Inference: A Sho |
35,281 | Textbook recommendations covering machine learning techniques for causal inference? | As dimitriy states, there isn't a singular textbook yet (or at least that I am aware of). However, there are a few textbook materials you can piece together to cover the topics you mentioned.
Targeted Learning in Data Science covers super learner (which is a generalized stacking algorithm you would almost always want ... | Textbook recommendations covering machine learning techniques for causal inference? | As dimitriy states, there isn't a singular textbook yet (or at least that I am aware of). However, there are a few textbook materials you can piece together to cover the topics you mentioned.
Targete | Textbook recommendations covering machine learning techniques for causal inference?
As dimitriy states, there isn't a singular textbook yet (or at least that I am aware of). However, there are a few textbook materials you can piece together to cover the topics you mentioned.
Targeted Learning in Data Science covers su... | Textbook recommendations covering machine learning techniques for causal inference?
As dimitriy states, there isn't a singular textbook yet (or at least that I am aware of). However, there are a few textbook materials you can piece together to cover the topics you mentioned.
Targete |
35,282 | Textbook recommendations covering machine learning techniques for causal inference? | For most recent work have a look at the conference for Causal Learning and Reasoning (CLeaR) 2022.
If you want to get started with ML and causal inference, I particular recommend (disclaimer: I m one of the co-authors) to look at Kelly, Kong, Goerg (2022) on "Predictive State Propensity Subclassification (PSPS): A caus... | Textbook recommendations covering machine learning techniques for causal inference? | For most recent work have a look at the conference for Causal Learning and Reasoning (CLeaR) 2022.
If you want to get started with ML and causal inference, I particular recommend (disclaimer: I m one | Textbook recommendations covering machine learning techniques for causal inference?
For most recent work have a look at the conference for Causal Learning and Reasoning (CLeaR) 2022.
If you want to get started with ML and causal inference, I particular recommend (disclaimer: I m one of the co-authors) to look at Kelly,... | Textbook recommendations covering machine learning techniques for causal inference?
For most recent work have a look at the conference for Causal Learning and Reasoning (CLeaR) 2022.
If you want to get started with ML and causal inference, I particular recommend (disclaimer: I m one |
35,283 | Why the standard error of the mean never gets to zero even when the sample has the same size as the population? | If the sample has the same size as the population, either some units from the population are sampled multiple times, or the sample is in fact the whole population.
In the former case, the sample mean provides an estimate of the population mean, in which case a different sample would likely yield a different estimate of... | Why the standard error of the mean never gets to zero even when the sample has the same size as the | If the sample has the same size as the population, either some units from the population are sampled multiple times, or the sample is in fact the whole population.
In the former case, the sample mean | Why the standard error of the mean never gets to zero even when the sample has the same size as the population?
If the sample has the same size as the population, either some units from the population are sampled multiple times, or the sample is in fact the whole population.
In the former case, the sample mean provides... | Why the standard error of the mean never gets to zero even when the sample has the same size as the
If the sample has the same size as the population, either some units from the population are sampled multiple times, or the sample is in fact the whole population.
In the former case, the sample mean |
35,284 | Why the standard error of the mean never gets to zero even when the sample has the same size as the population? | When sampling from finite population, the standard error of the mean $\frac{\sigma}{\sqrt{n}}$ must me multiplied with the finite population correction (FPC) $\sqrt{\frac{N - n}{N - 1}}$ where $N$ is the population size and $n$ the sample size. Hence, the corrected standard error of the mean is:
$$
\frac{\sigma}{\sqrt{... | Why the standard error of the mean never gets to zero even when the sample has the same size as the | When sampling from finite population, the standard error of the mean $\frac{\sigma}{\sqrt{n}}$ must me multiplied with the finite population correction (FPC) $\sqrt{\frac{N - n}{N - 1}}$ where $N$ is | Why the standard error of the mean never gets to zero even when the sample has the same size as the population?
When sampling from finite population, the standard error of the mean $\frac{\sigma}{\sqrt{n}}$ must me multiplied with the finite population correction (FPC) $\sqrt{\frac{N - n}{N - 1}}$ where $N$ is the popu... | Why the standard error of the mean never gets to zero even when the sample has the same size as the
When sampling from finite population, the standard error of the mean $\frac{\sigma}{\sqrt{n}}$ must me multiplied with the finite population correction (FPC) $\sqrt{\frac{N - n}{N - 1}}$ where $N$ is |
35,285 | Why the standard error of the mean never gets to zero even when the sample has the same size as the population? | The reason that this does not happen is that we apply formula's that assume that the sample size is much smaller than the population size.
A sample is often regarded as taken from an infinite population and, when used as an estimate for the mean of the population, the estimate of the mean of the population has an esti... | Why the standard error of the mean never gets to zero even when the sample has the same size as the | The reason that this does not happen is that we apply formula's that assume that the sample size is much smaller than the population size.
A sample is often regarded as taken from an infinite populat | Why the standard error of the mean never gets to zero even when the sample has the same size as the population?
The reason that this does not happen is that we apply formula's that assume that the sample size is much smaller than the population size.
A sample is often regarded as taken from an infinite population and,... | Why the standard error of the mean never gets to zero even when the sample has the same size as the
The reason that this does not happen is that we apply formula's that assume that the sample size is much smaller than the population size.
A sample is often regarded as taken from an infinite populat |
35,286 | Problems with cross-validation for time-ordered data | Your validation splits should match your target task. If taking a database, in a randomized fashion finding out sales prices and then filling the numbers for the rest is the task (without an intent to predict future sales), then simple cross-validation is just fine. If the goal is to predict future prices, then a past-... | Problems with cross-validation for time-ordered data | Your validation splits should match your target task. If taking a database, in a randomized fashion finding out sales prices and then filling the numbers for the rest is the task (without an intent to | Problems with cross-validation for time-ordered data
Your validation splits should match your target task. If taking a database, in a randomized fashion finding out sales prices and then filling the numbers for the rest is the task (without an intent to predict future sales), then simple cross-validation is just fine. ... | Problems with cross-validation for time-ordered data
Your validation splits should match your target task. If taking a database, in a randomized fashion finding out sales prices and then filling the numbers for the rest is the task (without an intent to |
35,287 | Problems with cross-validation for time-ordered data | I can't speak to the theoretical aspects, but why not preprocess your data to avoid the problem? If you can get some house price inflation data you can inflate the prices of the older (earlier timestamped) properties to values you can reasonably expect them to fetch today and then train your predictive algorithm using ... | Problems with cross-validation for time-ordered data | I can't speak to the theoretical aspects, but why not preprocess your data to avoid the problem? If you can get some house price inflation data you can inflate the prices of the older (earlier timesta | Problems with cross-validation for time-ordered data
I can't speak to the theoretical aspects, but why not preprocess your data to avoid the problem? If you can get some house price inflation data you can inflate the prices of the older (earlier timestamped) properties to values you can reasonably expect them to fetch ... | Problems with cross-validation for time-ordered data
I can't speak to the theoretical aspects, but why not preprocess your data to avoid the problem? If you can get some house price inflation data you can inflate the prices of the older (earlier timesta |
35,288 | Problems with cross-validation for time-ordered data | I don't think that having a time-stamp on the data is necessary problematic for housing price forecasting.
The procedure is to decompose the house into its attributes (size, location, etc.), add dummy variables for time and then do regression on these variables, this is called a "hedonic model" in the econometric liter... | Problems with cross-validation for time-ordered data | I don't think that having a time-stamp on the data is necessary problematic for housing price forecasting.
The procedure is to decompose the house into its attributes (size, location, etc.), add dummy | Problems with cross-validation for time-ordered data
I don't think that having a time-stamp on the data is necessary problematic for housing price forecasting.
The procedure is to decompose the house into its attributes (size, location, etc.), add dummy variables for time and then do regression on these variables, this... | Problems with cross-validation for time-ordered data
I don't think that having a time-stamp on the data is necessary problematic for housing price forecasting.
The procedure is to decompose the house into its attributes (size, location, etc.), add dummy |
35,289 | Problems with cross-validation for time-ordered data | One critical issue is the cage of covariance includes time varying effects, which can impose and unrecognised pattern on the data if not handled appropriately. There will be a general trends across house prices depending on the general economic situation evolving over time. more complex patterns such as evolving fashio... | Problems with cross-validation for time-ordered data | One critical issue is the cage of covariance includes time varying effects, which can impose and unrecognised pattern on the data if not handled appropriately. There will be a general trends across ho | Problems with cross-validation for time-ordered data
One critical issue is the cage of covariance includes time varying effects, which can impose and unrecognised pattern on the data if not handled appropriately. There will be a general trends across house prices depending on the general economic situation evolving ove... | Problems with cross-validation for time-ordered data
One critical issue is the cage of covariance includes time varying effects, which can impose and unrecognised pattern on the data if not handled appropriately. There will be a general trends across ho |
35,290 | In R, is it possible for pbinom to take a noninteger x? | The function is operating correctly, using the definition of the CDF. Consider a random variable $X$ with integer support. For any input $x+r$ with integer part $x$ and remainder $0 \leqslant r < 1$ you should get:
$$F(x+r) \equiv \mathbb{P}(X \leqslant x+r) = \mathbb{P}(X \leqslant x) = F(x).$$
Sure enough, that is ... | In R, is it possible for pbinom to take a noninteger x? | The function is operating correctly, using the definition of the CDF. Consider a random variable $X$ with integer support. For any input $x+r$ with integer part $x$ and remainder $0 \leqslant r < 1$ | In R, is it possible for pbinom to take a noninteger x?
The function is operating correctly, using the definition of the CDF. Consider a random variable $X$ with integer support. For any input $x+r$ with integer part $x$ and remainder $0 \leqslant r < 1$ you should get:
$$F(x+r) \equiv \mathbb{P}(X \leqslant x+r) = \... | In R, is it possible for pbinom to take a noninteger x?
The function is operating correctly, using the definition of the CDF. Consider a random variable $X$ with integer support. For any input $x+r$ with integer part $x$ and remainder $0 \leqslant r < 1$ |
35,291 | In R, is it possible for pbinom to take a noninteger x? | There are two ways of doing this.
Use a normal approximation to the sampling distribution of the sample mean.
Round the endpoints to the "closest" integer to get a conservative interval.
You may ask the teacher for clarity if this isn't clearly an application of some methods you've already covered.
EDIT:
Though R pro... | In R, is it possible for pbinom to take a noninteger x? | There are two ways of doing this.
Use a normal approximation to the sampling distribution of the sample mean.
Round the endpoints to the "closest" integer to get a conservative interval.
You may ask | In R, is it possible for pbinom to take a noninteger x?
There are two ways of doing this.
Use a normal approximation to the sampling distribution of the sample mean.
Round the endpoints to the "closest" integer to get a conservative interval.
You may ask the teacher for clarity if this isn't clearly an application of... | In R, is it possible for pbinom to take a noninteger x?
There are two ways of doing this.
Use a normal approximation to the sampling distribution of the sample mean.
Round the endpoints to the "closest" integer to get a conservative interval.
You may ask |
35,292 | Is it possible that marginally independent random variables are conditionally dependent? | Here a simple example with three Bernoulli random variables $X,Y,Z$ with the properties that
$X$ and $Z$ are independent, $Y$ and $Z$ are independent and
$X$ and $Z$ are conditionally dependent random variables given the value of $Y$,
$Y$ and $Z$ are conditionally dependent random variables given the value of $X$,
... | Is it possible that marginally independent random variables are conditionally dependent? | Here a simple example with three Bernoulli random variables $X,Y,Z$ with the properties that
$X$ and $Z$ are independent, $Y$ and $Z$ are independent and
$X$ and $Z$ are conditionally dependent rand | Is it possible that marginally independent random variables are conditionally dependent?
Here a simple example with three Bernoulli random variables $X,Y,Z$ with the properties that
$X$ and $Z$ are independent, $Y$ and $Z$ are independent and
$X$ and $Z$ are conditionally dependent random variables given the value of... | Is it possible that marginally independent random variables are conditionally dependent?
Here a simple example with three Bernoulli random variables $X,Y,Z$ with the properties that
$X$ and $Z$ are independent, $Y$ and $Z$ are independent and
$X$ and $Z$ are conditionally dependent rand |
35,293 | Is it possible that marginally independent random variables are conditionally dependent? | @Dilip Sarwate: have already given a good answer, the purpose of this is to show the connection with Simpson's paradox. If such examples did not exist, Simpson's paradox would have been eliminated. The easy way to see is with a simple example with artificial data. Let $X, Y, Z$ have the possible values $-1, +1$. In the... | Is it possible that marginally independent random variables are conditionally dependent? | @Dilip Sarwate: have already given a good answer, the purpose of this is to show the connection with Simpson's paradox. If such examples did not exist, Simpson's paradox would have been eliminated. Th | Is it possible that marginally independent random variables are conditionally dependent?
@Dilip Sarwate: have already given a good answer, the purpose of this is to show the connection with Simpson's paradox. If such examples did not exist, Simpson's paradox would have been eliminated. The easy way to see is with a sim... | Is it possible that marginally independent random variables are conditionally dependent?
@Dilip Sarwate: have already given a good answer, the purpose of this is to show the connection with Simpson's paradox. If such examples did not exist, Simpson's paradox would have been eliminated. Th |
35,294 | Standard Error, Standard Deviation and Variance confusion | The term "standard error" refers to the standard deviation of a statistic that is calculated. So, you can calculate a standard error for a mean--because the mean is a statistic. You can also calculate a standard error for a parameter estimate like $\hat{\beta}$.
We say standard error instead of standard deviation to di... | Standard Error, Standard Deviation and Variance confusion | The term "standard error" refers to the standard deviation of a statistic that is calculated. So, you can calculate a standard error for a mean--because the mean is a statistic. You can also calculate | Standard Error, Standard Deviation and Variance confusion
The term "standard error" refers to the standard deviation of a statistic that is calculated. So, you can calculate a standard error for a mean--because the mean is a statistic. You can also calculate a standard error for a parameter estimate like $\hat{\beta}$.... | Standard Error, Standard Deviation and Variance confusion
The term "standard error" refers to the standard deviation of a statistic that is calculated. So, you can calculate a standard error for a mean--because the mean is a statistic. You can also calculate |
35,295 | Standard Error, Standard Deviation and Variance confusion | The terminology is the same everywhere in statistics I think:
Variance $\sigma^2$ is the second moment of a known probability distribution
Standard Deviation $\sigma$ is the square root of variance
Variance of the mean $\sigma^2_{\mu} = \frac{\sigma^2}{N}$ is the variance of the mean of $N$ i.i.d random variables
Stan... | Standard Error, Standard Deviation and Variance confusion | The terminology is the same everywhere in statistics I think:
Variance $\sigma^2$ is the second moment of a known probability distribution
Standard Deviation $\sigma$ is the square root of variance
V | Standard Error, Standard Deviation and Variance confusion
The terminology is the same everywhere in statistics I think:
Variance $\sigma^2$ is the second moment of a known probability distribution
Standard Deviation $\sigma$ is the square root of variance
Variance of the mean $\sigma^2_{\mu} = \frac{\sigma^2}{N}$ is t... | Standard Error, Standard Deviation and Variance confusion
The terminology is the same everywhere in statistics I think:
Variance $\sigma^2$ is the second moment of a known probability distribution
Standard Deviation $\sigma$ is the square root of variance
V |
35,296 | A good book for regression analysis for pure mathematicians | I would recommend Seber & Lee (from which I originally learned regression.) Cover most of your topics with proofs. An alternative in the same style, but also covering glm's is Linear Models and Generalizations : Least Squares and Alternatives by Rao et al.
A shorter book with a more geometric viewpoint is The Coordina... | A good book for regression analysis for pure mathematicians | I would recommend Seber & Lee (from which I originally learned regression.) Cover most of your topics with proofs. An alternative in the same style, but also covering glm's is Linear Models and Gener | A good book for regression analysis for pure mathematicians
I would recommend Seber & Lee (from which I originally learned regression.) Cover most of your topics with proofs. An alternative in the same style, but also covering glm's is Linear Models and Generalizations : Least Squares and Alternatives by Rao et al.
A ... | A good book for regression analysis for pure mathematicians
I would recommend Seber & Lee (from which I originally learned regression.) Cover most of your topics with proofs. An alternative in the same style, but also covering glm's is Linear Models and Gener |
35,297 | A good book for regression analysis for pure mathematicians | I think that you might be interested in
Stachurski (2016) A Primer in Econometric Theory.
The book is quite mathematically oriented. The book is organized into 3 sections:
Background - which is all about pure mathematical foundations; vector spaces, linear algebra and matrices, foundations of probability, modeling dep... | A good book for regression analysis for pure mathematicians | I think that you might be interested in
Stachurski (2016) A Primer in Econometric Theory.
The book is quite mathematically oriented. The book is organized into 3 sections:
Background - which is all a | A good book for regression analysis for pure mathematicians
I think that you might be interested in
Stachurski (2016) A Primer in Econometric Theory.
The book is quite mathematically oriented. The book is organized into 3 sections:
Background - which is all about pure mathematical foundations; vector spaces, linear al... | A good book for regression analysis for pure mathematicians
I think that you might be interested in
Stachurski (2016) A Primer in Econometric Theory.
The book is quite mathematically oriented. The book is organized into 3 sections:
Background - which is all a |
35,298 | Why can I interpret a log transformed dependent variable in terms of percent change in linear regression? | Say we have a model like this:
$$\log\hat y=\beta_0+\beta_1 x$$
Since $\exp$ is the inverse function of $\log$, we can do this:
$$\hat y = f(x)=\exp(\beta_0+\beta_1 x)$$
Now, what happens when $x$ grows by 1? $f(x)$ multiplies by $\exp(\beta_1)$:
$$\begin{align}
f(x+1)&=\exp[\beta_0+\beta_1(x+1)]\\
&=\exp(\beta_0+\beta... | Why can I interpret a log transformed dependent variable in terms of percent change in linear regres | Say we have a model like this:
$$\log\hat y=\beta_0+\beta_1 x$$
Since $\exp$ is the inverse function of $\log$, we can do this:
$$\hat y = f(x)=\exp(\beta_0+\beta_1 x)$$
Now, what happens when $x$ gro | Why can I interpret a log transformed dependent variable in terms of percent change in linear regression?
Say we have a model like this:
$$\log\hat y=\beta_0+\beta_1 x$$
Since $\exp$ is the inverse function of $\log$, we can do this:
$$\hat y = f(x)=\exp(\beta_0+\beta_1 x)$$
Now, what happens when $x$ grows by 1? $f(x)... | Why can I interpret a log transformed dependent variable in terms of percent change in linear regres
Say we have a model like this:
$$\log\hat y=\beta_0+\beta_1 x$$
Since $\exp$ is the inverse function of $\log$, we can do this:
$$\hat y = f(x)=\exp(\beta_0+\beta_1 x)$$
Now, what happens when $x$ gro |
35,299 | Can Negative Binomial parameters be treated like Poisson? | I put up some code to perform this task in PyMC3, since you mentioned it in the question. The first part, which you seem to already be familiar with, would be fitting the model to get a posterior distribution on the parameters:
import pymc3 as pm
import numpy as np
# generating simulated data data for a week
data = pm... | Can Negative Binomial parameters be treated like Poisson? | I put up some code to perform this task in PyMC3, since you mentioned it in the question. The first part, which you seem to already be familiar with, would be fitting the model to get a posterior dist | Can Negative Binomial parameters be treated like Poisson?
I put up some code to perform this task in PyMC3, since you mentioned it in the question. The first part, which you seem to already be familiar with, would be fitting the model to get a posterior distribution on the parameters:
import pymc3 as pm
import numpy as... | Can Negative Binomial parameters be treated like Poisson?
I put up some code to perform this task in PyMC3, since you mentioned it in the question. The first part, which you seem to already be familiar with, would be fitting the model to get a posterior dist |
35,300 | Can Negative Binomial parameters be treated like Poisson? | The negative binomial can be treated like Poisson, but it is ambiguous how to treat it. It will depend on the underlying process that causes the overdispersion. This can occur in different ways.
Below I will describe two ways:
The negative binomial occurs as a Poisson distribution compounded with a gamma distribution
... | Can Negative Binomial parameters be treated like Poisson? | The negative binomial can be treated like Poisson, but it is ambiguous how to treat it. It will depend on the underlying process that causes the overdispersion. This can occur in different ways.
Below | Can Negative Binomial parameters be treated like Poisson?
The negative binomial can be treated like Poisson, but it is ambiguous how to treat it. It will depend on the underlying process that causes the overdispersion. This can occur in different ways.
Below I will describe two ways:
The negative binomial occurs as a ... | Can Negative Binomial parameters be treated like Poisson?
The negative binomial can be treated like Poisson, but it is ambiguous how to treat it. It will depend on the underlying process that causes the overdispersion. This can occur in different ways.
Below |
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