Timeline for Python statsmodels, handling over-dispersion for Poisson Regression
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| May 23, 2021 at 2:37 | comment | added | Josef | The marginal distribution of y will not be Poisson anymore if there is unobserved heterogeneity. NegativeBinomial is the marginal distribution if the mixing distribution for unobserved heterogeneity is Gamma. | |
| May 23, 2021 at 2:33 | comment | added | Josef |
The Poisson model can still consistently estimate the mean parameters even with excess dispersion. However, the standard errors for the parameters need to be adjusted. One standard way is to use pearson chisquare as estimate for the dispersion (scale). That's separately estimated and not part of the summary parameter table.
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| May 23, 2021 at 2:29 | comment | added | Josef |
discrete models NegativeBinomial and GeneralizedPoisson do not have fit_constrained yet, so the model would need to be reparameterized, for example by dropping the constant.
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| May 23, 2021 at 2:28 | comment | added | Will_E |
I want to preserve the Poisson distribution for the $y_e$ term but add another coefficient in my summary table, so instead of reporting back three coefs for CATEGORY_1, CATEGORY_2, and CATEGORY_3 - I would have four, adding an alpha coef that highlights the overdispersion
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| May 23, 2021 at 2:25 | comment | added | Josef | there is currently no model that uses mixing of Poisson with normal heterogeneity, unless it's clustered data. Beside NegativeBinomial, statsmodels.org/dev/generated/… also allows for overdispersion. | |
| May 23, 2021 at 2:09 | review | First posts | |||
| May 23, 2021 at 6:28 | |||||
| May 23, 2021 at 2:08 | comment | added | usεr11852 |
0. Welcome to CV.SE. 1.Why not use a negative binomial model through discrete_model.NegativeBinomial?
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| May 23, 2021 at 2:03 | history | asked | Will_E | CC BY-SA 4.0 |