Timeline for Negative Binomial Regression Model - Effect of Removing Significant Covariates
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 1, 2023 at 15:35 | history | edited | Barton | CC BY-SA 4.0 | deleted 44 characters in body |
| Oct 26, 2023 at 10:07 | history | edited | Barton | CC BY-SA 4.0 | added 44 characters in body |
| Oct 26, 2023 at 10:04 | comment | added | Barton | When I generate Poisson count variables, I fit them using Poisson regression (no overdispersion inherent in data). Removing a known significant covariate clearly has an impact on all fronts. Conversely, when NB2 count variables are generated and fit using NB2 regression, this effect is not observed - except in LL values. | |
| Oct 26, 2023 at 9:27 | comment | added | dimitriy | Could this be because NB requires that the NB distribution be fully correct, including a specific variance/mean relationship, while the Poisson quasi-MLE doesn’t? | |
| Oct 26, 2023 at 8:40 | history | edited | Barton | CC BY-SA 4.0 | deleted 22 characters in body |
| Oct 25, 2023 at 19:21 | answer | added | Peter Flom | timeline score: 1 | |
| Oct 25, 2023 at 13:53 | history | edited | Barton | CC BY-SA 4.0 | edited title |
| S Oct 25, 2023 at 13:52 | review | First questions | |||
| Oct 25, 2023 at 15:11 | |||||
| S Oct 25, 2023 at 13:52 | history | asked | Barton | CC BY-SA 4.0 |