Timeline for US states - fixed or random effect?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 20 at 7:04 | comment | added | Closed Limelike Curves | It also doesn’t really matter what loss function you pick. Asymptotically, no regularization always loses to regularized estimation. | |
| Feb 20 at 7:00 | comment | added | Closed Limelike Curves | @ShawnHemelstrand I mean out of sample MSE, or MSE of regression coefficients, not MSE of in-sample predictions. I suggest googling “Stein’s estimator.” | |
| Feb 17 at 5:38 | comment | added | Shawn Hemelstrand | The mean squared error shouldn't be the only criterion for selecting fixed and random effects. If MSE was the only reason to run models, then overfitting regressions would be the norm in statistics. | |
| Feb 17 at 4:57 | history | edited | Closed Limelike Curves | CC BY-SA 4.0 |
added 58 characters in body
|
| Feb 17 at 4:57 | comment | added | Closed Limelike Curves | My point isn't that fixed effects are never useful--they're definitely an improvement on no effects! But Stein's phenomenon shows that the random effects estimator will always have a smaller mean squared error than the fixed effects estimator. | |
| Feb 17 at 4:53 | comment | added | Closed Limelike Curves | @ShawnHemelstrand Random effects regression is a kind of regression. | |
| Feb 13 at 1:09 | comment | added | Shawn Hemelstrand | There’s really no good reason to use fixed effects for anything, except to save effort. I don't understand this comment. Can you elaborate? Why would anybody use regression if fixed effects are never useful? | |
| Feb 13 at 0:16 | history | answered | Closed Limelike Curves | CC BY-SA 4.0 |