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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?

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