Beyond support in two-stage variable selection

Abstract : Numerous variable selection methods rely on a two-stage procedure, where a sparsity-inducing penalty is used in the first stage to predict the support, which is then conveyed to the second stage for estimation or inference purposes. In this framework, the first stage screens variables to find a set of possibly relevant variables and the second stage operates on this set of candidate variables, to improve estimation accuracy or to assess the uncertainty associated to the selection of variables. We advocate that more information can be conveyed from the first stage to the second one: we use the magnitude of the coefficients estimated in the first stage to define an adaptive penalty that is applied at the second stage. We give the example of an inference procedure that highly benefits from the proposed transfer of information. The procedure is precisely analyzed in a simple setting, and our large-scale experiments empirically demonstrate that actual benefits can be expected in much more general situations, with sensitivity gains ranging from 50 to 100 % compared to state-of-the-art.
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Jean-Michel Bécu, Yves Grandvalet, Christophe Ambroise, Cyril Dalmasso. Beyond support in two-stage variable selection. Statistics and Computing, Springer Verlag (Germany), 2017, 27 (1), pp.169--179. ⟨10.1007/s11222-015-9614-1⟩. ⟨hal-01246066⟩

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