Combined l1 and Greedy l0 Penalized Least Squares for Linear Model Selection

We introduce a computationally effective algorithm for a linear model selection consisting of three steps: screening--ordering-- selection (SOS). Screening of predictors is based on the thresholded Lasso that is $\ell_1$ penalized least squares. The screened predictors are then fitted using least squares (LS) and ordered with respect to their $|t|$ statistics. Finally, a model is selected using greedy generalized information criterion (GIC) that is $\ell_0$ penalized LS in a nested family induced by the ordering. We give non-asymptotic upper bounds on error probability of each step of the SOS algorithm in terms of both penalties. Then we obtain selection consistency for different ($n$, $p$) scenarios under conditions which are needed for screening consistency of the Lasso. Our error bounds and numerical experiments show that SOS is worth considering alternative for multi-stage convex relaxation, the latest quasiconvex penalized LS. For the traditional setting ($n >p$) we give Sanov-type bounds on the error probabilities of the ordering--selection algorithm. It is surprising consequence of our bounds that the selection error of greedy GIC is asymptotically not larger than of exhaustive GIC. [abs] [ pdf ][ bib ] © JMLR 2015. (edit, beta)