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Preprints, Working Papers, ... Year : 2019

The revisited knockoffs method for variable selection in $L_1$-penalised regressions

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 $L_1$-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.
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Dates and versions

hal-01799914 , version 1 (25-05-2018)
hal-01799914 , version 2 (17-11-2019)

Identifiers

  • HAL Id : hal-01799914 , version 2

Cite

Anne Gégout-Petit, Aurélie Muller-Gueudin, Clémence Karmann. The revisited knockoffs method for variable selection in $L_1$-penalised regressions. 2019. ⟨hal-01799914v2⟩
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