Timeline for Significance test of the amplitude of sinusoidal regression
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 16, 2023 at 1:03 | vote | accept | Dominik Rolph | ||
| Oct 15, 2023 at 20:58 | comment | added | Ben | Sorry, I had thought your post referred to linear rather than logistic regression. Yes the appropriate test in this case is the likelihood ratio test using the chi-squared test statistic. | |
| Oct 15, 2023 at 6:56 | comment | added | Dominik Rolph | It seems like the partial F-test is used for multiple regression and based on your reference, can be derived from the likelihood ratio test. The equivalent likelihood ratio test for mixed effects models seems to be approximately $\chi^2$ distributed. With this comment I want to clarify - for the amplitude test, do you refer to a model comparison procedure or did you suggest something different? | |
| Oct 13, 2023 at 8:47 | comment | added | Ben | For the amplitude test, you would do a partial F-test on the two regression coefficients $w_1$ and $w_2$ together (not look at their individual p-values in t-tests) (see here). The phase angle test would be a bit more complicated (see here for some preliminary thoughts). | |
| Oct 12, 2023 at 17:28 | comment | added | Dominik Rolph | This is helpful. Could you elaborate on both tests? For the amplitude test, does that you would just check the p-value of each of the regression weights (representing sine and cosine) and call the amplitude significant if at least one of the regressors are significant? | |
| Oct 12, 2023 at 7:08 | history | answered | Ben | CC BY-SA 4.0 |