Timeline for How to deal with overdispersion in Poisson regression: quasi-likelihood, negative binomial GLM, or subject-level random effect?
Current License: CC BY-SA 3.0
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| May 18, 2023 at 18:58 | comment | added | ThighCrush | Sorry, I'm talking about using a Poisson GLMM instead of the negative binomial, if that wasn't clear. | |
| May 18, 2023 at 17:10 | comment | added | AdamO | @ThighCrush are you saying that a negative binomial model is a GLMM? The negative binomial model is (arguably) a GLM which can be extended (as a GLMM) to take account of random effects. Otherwise, a NB model is generally considered a model for independent data. | |
| May 18, 2023 at 16:52 | comment | added | ThighCrush | I agree that probably the main reason for using mixed models is to account for dependence structures, but you seem to say GLMMs are not appropriate for modeling overdispersed count data? I don't see why not; after all, the negative binomial model can be seen as a poisson model with a gamma prior on the mean; hence if we add a random intercept term to the predictor to account for missing covariates, such that the random effect has a different value for each observation, doesn't that also just amount to a Poisson mixture with a lognormal (taking the canonical link) prior on the mean lambda? | |
| May 20, 2022 at 15:10 | comment | added | Suriname0 | I believe the corrected link for the tutorial is the following: online.stat.psu.edu/stat504/lesson/9/9.2-0 | |
| Mar 17, 2018 at 16:38 | vote | accept | Bryan | ||
| Mar 8, 2018 at 14:18 | history | edited | AdamO | CC BY-SA 3.0 |
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| Mar 7, 2018 at 17:53 | history | edited | AdamO | CC BY-SA 3.0 |
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| Mar 7, 2018 at 17:40 | history | edited | AdamO | CC BY-SA 3.0 |
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| Mar 7, 2018 at 17:33 | history | answered | AdamO | CC BY-SA 3.0 |