# Tag Info

Accepted

### Continuous generalization of the negative binomial distribution

That's an interesting question. My research group has been using the distribution you refer to for some years in our publicly available bioinformatics software. As far as I know, the distribution does ...
• 13.2k
Accepted

### How to deal with overdispersion in Poisson regression: quasi-likelihood, negative binomial GLM, or subject-level random effect?

Poisson regression is just a GLM: People often speak of the parametric rationale for applying Poisson regression. In fact, Poisson regression is just a GLM. That means Poisson regression is justified ...
• 63.7k
Accepted

### Expected number of times to roll a die until each side has appeared 3 times

Suppose all $d=6$ sides have equal chances. Let's generalize and find the expected number of rolls needed until side $1$ has appeared $n_1$ times, side $2$ has appeared $n_2$ times, ..., and side $d$ ...
• 329k

### What is the appropriate model for underdispersed count data?

The best --- and standard ways to handle underdispersed Poisson data is by using a generalized Poisson, or perhaps a hurdle model. Three parameter count models can also be used for underdispersed data;...
• 251

### Interpretation of .L & .Q output from a negative binomial GLM with categorical data

Your variables aren't just coded as factors (to make them categorical), they are coded as ordered factors. Then, by default, R fits a series of polynomial functions to the levels of the variable. ...

### Diagnostic plots for count regression

This is an old question, but I thought it would be useful to add that my DHARMa R package (available from CRAN, see here) now provides standardized residuals for GLMs and GLMMs, based on a simulation ...
• 8,339

### Diagnostics for generalized linear (mixed) models (specifically residuals)

This is an old question, but I thought it would be useful to add that option 4 suggested by the OP is now available in the DHARMa R package (available from CRAN, see here). The package makes the ...
• 8,339
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• 140k
Accepted

### Zero-inflated Gaussian for weights below zero recorded as 0?

I think the model is more appropriately a left-censored Gaussian, since the process you describe is about discarding information below some value (in this case, the location is known to be 0, which is ...
• 92.6k
Accepted

### Poisson Gamma Mixture = Negative Binomially Distributed?

There are various ways a negative binomial distribution can come about. One of them, as Robert Long comments, is as a Poisson distribution whose parameter is itself Gamma distributed. The Wikipedia ...
• 127k
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### Dealing with heteroskedasticity in negative binomial GLM

This answer (Negative Binomial Regression and Heteroskedasticity) on the same forum explains very nicely that models such as yours are predicated on a certain type of relationship between the (...
• 20.6k

### Standard negative binomial regression when counts are mainly zeros?

I don't quite think that a distinction between "true" and "untrue" ("false"?) zeros is very helpful. Zero inflated distributions arise naturally if your data generating ...
• 127k
Accepted

### conditional on the total, what is the distribution of negative binomials

Sorry for the late answer, but this bugged me as well and I found the answer. The distribution is indeed Dirichlet-Multinomial and the individual neg. binomial distributions don't even need to be ...
• 2,637

### Negative Binomial Regression and Heteroskedasticity

Heteroskedasticity is relevant with ordinary linear regression, where there is an assumption that variance is constant (do not depend on the mean), known as homoskedasticity. But with alternative ...
• 81.5k

### How is a negative binomial regression model different from OLS with a logged outcome variable?

Assuming you've already identified the predictor(s) of interest/importance, the considerations for a model (in approximate order of importance) would be: a. do you want to model the conditional mean ...
• 286k