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Why can't we use AIC and p-value variable selection within the same model building exercise?

AIC is more a model selection method in which you do not favour some null hypothesis. Contrary to this, with a hypothesis test (and with p-values) you choose to reject or not reject a null hypothesis ...
• 46.3k
1 vote

How to select predictor variables for linear mixed model?

Are all your variables binary (0,1)? If some aren't really binary but you've made it so, please return them to factors. (i.e. n_stage, t_stage). There are may strategies to perform a model. In this ...
1 vote

Forcing covariates to always be part of a Lasso model

Lasso by default adds a regularization penalty for all the parameters, but nothing prohibits you from penalizing only some of the parameters. Running lasso and "adding back" the zeroed-out ...
• 113k

Unintuitive results in model comparison

This is mostly an extended comment on the answer from @Christian Hennig (+1), which covers the critical points and which I think you should accept. It's very tempting to think that a "significant ...
• 62k
Accepted

Unintuitive results in model comparison

If you're running lots of tests, the probability that you occasionally find significant results even if nothing meaningful goes on is quite high. Andrew Gelman and Hal Stern show here... https://www....
• 11.4k
1 vote

Equivalence of AIC and p-values in model selection

Maybe some detailed explanation about the excellent answer of @Frank Harrell The test statistic of a Likelihood-ratio test (LRT) is defined as (Wikipedia)  \lambda_{\text{LR}} = -2(\ell_0 - \ell_A) \$...
• 111