Timeline for how to deal with limited observations of categorical independent variables during logistic regression?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 14, 2021 at 9:00 | history | tweeted | twitter.com/StackStats/status/1360876441265700864 | ||
| Oct 24, 2017 at 15:17 | comment | added | Matthew Drury | Combining the two ideas, you could encode into broader conceptual categories of employment, then use these as fixed effects in a mixed effect model that includes the original fine grained categories as random effects. This gets you a nice interpretable model, and give a bit of regularization to deal with overfitting from the rare class issue. | |
| Oct 24, 2017 at 14:56 | history | edited | pd441 | CC BY-SA 3.0 |
deleted 89 characters in body
|
| May 16, 2017 at 10:41 | comment | added | Wes | Have you considered recoding the employment variable into broader categories? Using categories of employment is common practice and can still be relevant depending on the phenomenon of interest. Otherwise it is hard to see how you can infer much from categories with single observations. | |
| May 16, 2017 at 10:22 | comment | added | kjetil b halvorsen♦ | This is a use case for regularization with fused lasso, see stats.stackexchange.com/questions/227125/… and links therein. | |
| May 16, 2017 at 10:21 | history | edited | kjetil b halvorsen♦ | CC BY-SA 3.0 |
deleted 41 characters in body; edited tags
|
| May 16, 2017 at 7:27 | review | First posts | |||
| May 16, 2017 at 9:05 | |||||
| May 16, 2017 at 7:24 | history | asked | pd441 | CC BY-SA 3.0 |