Timeline for Poisson model appears overdispersed, but usual recommended approaches don't improve fit
Current License: CC BY-SA 4.0
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when toggle format | what | by | license | comment | |
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Apr 8, 2019 at 10:53 | answer | added | NB12 | timeline score: 1 | |
Apr 6, 2019 at 9:40 | vote | accept | user2390246 | ||
Apr 3, 2019 at 21:35 | answer | added | kjetil b halvorsen♦ | timeline score: 2 | |
Apr 3, 2019 at 21:22 | history | edited | kjetil b halvorsen♦ |
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Apr 2, 2019 at 14:41 | comment | added | Glen_b | With a Poisson model, the skewness and the proportion of zeros are too high given the conditional mean (and the variance is considerably too high), but for a negative binomial there's less concern about them (it's not perfect by any means, but somewhat better). I'd caution against being overly focused on hypothesis testing of assumptions; hypothesis tests don't answer the right question, but instead give an inaccurate response to a question you already know the answer to. Such tests lead you to "fix" non problems and relax about things that matter. | |
Apr 2, 2019 at 13:22 | comment | added | user2390246 | @Glen_b Thanks for your comment. Are you saying that the skewness of residuals I'm seeing here is within acceptable bounds, or that there is some other problem unrelated to overdispersion that I have not yet diagnosed? (Zero-inflation seems to be marginal, according to a vuong test comparing negbin against zinb model). | |
Apr 2, 2019 at 11:38 | comment | added | Glen_b | Either Anscombe or deviance residuals should look closer to normal than say Pearson or working residuals (but also don't expect them to look actually normal). You are getting deviance residuals, so that's fine. You have more skewness than you'd expect from a Poisson; you're right that it's overdispersed, but an overdispersed Poisson cannot change the skewness of the residuals.) .... I think the negative binomial model should be adequate but you can always look at a zero-inflated distribution. | |
Apr 2, 2019 at 11:25 | history | asked | user2390246 | CC BY-SA 4.0 |