  We consider the problem of variable selection in regression models. In
particular, we are interested in selecting explanatory covariates linked with
the response variable and we want to determine which covariates are relevant,
that is which covariates are involved in the model. In this framework, we deal
with L1-penalised regression models. To handle the choice of the penalty
parameter to perform variable selection, we develop a new method based on the
knockoffs idea. This revisited knockoffs method is general, suitable for a wide
range of regressions with various types of response variables. Besides, it also
works when the number of observations is smaller than the number of covariates
and gives an order of importance of the covariates. Finally, we provide many
experimental results to corroborate our method and compare it with other
variable selection methods.
