Say we are fitting a penalized model, such as a linear regression with lasso regularization. We expect to obtain a model with the most significant covariables.
The method starts with many covariables and ends up with just a few, as we use to do with backwards stepwise methods, but the result is obtained in a single step.
Can it be considered some sort of indirect multitesting and then it should need some kind of correction for the p-values, such as Bonferroni?
