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

## TL;DR

This paper introduces a generalized knockoffs-based method for variable selection in L1-penalized regression models, effective across different response types and with high-dimensional data, supported by extensive experiments.

## Contribution

It develops a new, versatile knockoffs approach for variable selection in L1-penalized regressions, handling various response types and high-dimensional settings.

## Key findings

- Effective in small sample sizes
- Performs well across different regression types
- Provides a meaningful importance ranking of variables

## Abstract

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.