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We consider the problem of variable selection in regression models. Inparticular, we are interested in selecting explanatory covariates linked withthe response variable and we want to determine which covariates are relevant,that is which covariates are involved in the model. In this framework, we dealwith L1-penalised regression models. To handle the choice of the penaltyparameter to perform variable selection, we develop a new method based on theknockoffs idea. This revisited knockoffs method is general, suitable for a widerange of regressions with various types of response variables. Besides, it alsoworks when the number of observations is smaller than the number of covariatesand gives an order of importance of the covariates. Finally, we provide manyexperimental results to corroborate our method and compare it with othervariable selection methods.

The revisited knockoffs method for variable selection in L1-penalised regressions