Timeline for Does general linear model includes interaction?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 15, 2023 at 14:19 | vote | accept | POC | ||
| Oct 9, 2023 at 14:26 | comment | added | Ben Bolker | Andrew Gelman points out that the power to detect an interaction is usually much lower than that to detect main effects: statmodeling.stat.columbia.edu/2018/03/15/need16 ... maybe that's what you're after? | |
| Oct 9, 2023 at 12:09 | comment | added | POC | Maybe the word i'm looking for is efficient rather than convergence @Glen_b. Would the efficiency be the same for all $\mathbf{B}$ if the distributions in $\mathbf{X}$ are different (like the interaction terms)? Maybe, it should be a question altogether. | |
| Oct 9, 2023 at 9:15 | comment | added | carlo | The composition of X does affect convergence of the fitting algorithm in generalized linear models, because of collinearity. Note that this is not the same as convergence of a sample to any distribution, and also doesn't quite apply to the formula you posted, because in that case you have to worry that $X^TX$ is invertible (there is no multi-step fitting algorithm, and thus no convergence or divergence). | |
| Oct 9, 2023 at 2:32 | comment | added | Glen_b | @POC convergence of what, exactly? | |
| Oct 9, 2023 at 0:04 | comment | added | Ben Bolker | The general linear model typically assumes a Gaussian conditional distribution. Other conditional distributions would suggest that you're working with a generalized (rather than general) linear model, at which points questions about convergence etc. could get much more complicated. | |
| Oct 8, 2023 at 23:10 | comment | added | POC | Thank you. Would the convergence varies depending on the distributions? | |
| Oct 8, 2023 at 22:48 | history | answered | Ben Bolker | CC BY-SA 4.0 |