Timeline for Is it true that currently we cannot compute p-values or confidence intervals for the coefficients from a Lasso regression problem?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 16, 2022 at 14:15 | comment | added | Frank Harrell | The scale factor on the Laplace prior for the Bayesian lasso is exactly the reciprocal of lasso's $\lambda$. The question is how do you solve for $\lambda$ with lasso or how do you select/pre-specify $\lambda$ for Bayes. | |
| Aug 16, 2022 at 13:40 | comment | added | Demetri Pananos | @FrankHarrell I’m not sure I follow. Cross validation is usually the method for selecting the lambda, where as either prior knowledge or prior predictive checks are the method for determining the scaling factor. How are these equivalent? | |
| Aug 16, 2022 at 13:24 | comment | added | Frank Harrell | The intuition required to specify the scaling factor for the priors is exactly the same challenge of finding the value of the lasso penalty factor $\lambda$. | |
| Aug 16, 2022 at 13:12 | comment | added | Demetri Pananos | @FrankHarrell Makes a good point (though it should be mentioned that the posterior medians correspond to the estimate resulting from a LASSO). However, doing a Bayesian sort of Lasso would require some intuition on the variance of the priors, which correspond to the regularizing strength. | |
| Aug 15, 2022 at 11:49 | comment | added | Frank Harrell | If you run a Bayesian lasso (or any other penalized regression) you automatically get the full inference machine. Posterior distributions are interpreted as simply as if there were no penalization. It's the frequentist setting in which things are very complicated. | |
| Aug 15, 2022 at 7:11 | history | answered | Demetri Pananos | CC BY-SA 4.0 |