Timeline for Mean adjusted $R^2$ for linear regression with gaussian noise covariates
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
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| S Feb 13, 2018 at 2:16 | history | bounty ended | CommunityBot | ||
| S Feb 13, 2018 at 2:16 | history | notice removed | CommunityBot | ||
| Feb 10, 2018 at 20:47 | history | tweeted | twitter.com/StackStats/status/962427766149959681 | ||
| Feb 6, 2018 at 6:02 | answer | added | Ben | timeline score: 1 | |
| S Feb 5, 2018 at 1:12 | history | bounty started | Olivier | ||
| S Feb 5, 2018 at 1:12 | history | notice added | Olivier | Draw attention | |
| Feb 3, 2018 at 19:12 | comment | added | Richard Hardy | I guess you also want the Gaussian noise to be irrelevant in the sense that the population regression coefficient for it would be zero. Otherwise you can have Gaussian noise that is relevant. If all regressors are irrelevant, $R^2_{adj.}$ is an unbiased and consistent estimator of the population $R^2$; see Dave Giles blog post "In What Sense is the "Adjusted" R-Squared Unbiased?". However, I am not sure what happens when some of the regressors are relevant... | |
| Feb 3, 2018 at 19:08 | history | edited | Richard Hardy |
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| Feb 3, 2018 at 2:17 | history | edited | Olivier | CC BY-SA 3.0 |
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| Feb 3, 2018 at 2:07 | history | edited | Olivier | CC BY-SA 3.0 |
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| Feb 3, 2018 at 1:11 | history | asked | Olivier | CC BY-SA 3.0 |