Timeline for Should I use unpenalized logistic regression, lasso or ridge for explanatory modelling?
Current License: CC BY-SA 3.0
9 events
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| Oct 27, 2020 at 23:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Sep 25, 2020 at 20:41 | answer | added | rolando2 | timeline score: 1 | |
| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/
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| Jan 23, 2017 at 6:27 | comment | added | Richard Hardy | Probably yes. But it would be difficult to determine the standard errors and statistical significance of coefficients estimated in this way. | |
| Jan 23, 2017 at 1:34 | comment | added | jay | This post blog.datadive.net/… discusses the benefits and of ridge vs lasso for variable importance. It suggests ridge may be better than lasso when important predictors are correlated (lasso can be unstable as it zeros important predictors out), but that stability selection is often the best approach. So, if I use lasso + stability selection to find important variables, then run ridge regression on the important subset, would this be a good way to get stable coefficients for important variables? | |
| Jan 13, 2017 at 17:43 | history | tweeted | twitter.com/StackStats/status/819963192172494849 | ||
| Jan 12, 2017 at 17:11 | history | edited | Richard Hardy | CC BY-SA 3.0 |
edited tags; edited title
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| Jan 12, 2017 at 17:10 | comment | added | Richard Hardy | I agree with your last sentence. | |
| Jan 12, 2017 at 2:10 | history | asked | jay | CC BY-SA 3.0 |