Variable selection and estimation in generalized linear models with the seamless L₀ penalty.

ABSTRACT: In this paper, we propose variable selection and estimation in generalized linear models using the seamless L₀ (SELO) penalized likelihood approach. The SELO penalty is a smooth function that very closely resembles the discontinuous L₀ penalty. We develop an e cient algorithm to fit the model, and show that the SELO-GLM procedure has the oracle property in the presence of a diverging number of variables. We propose a Bayesian Information Criterion (BIC) to select the tuning parameter. We show that under some regularity conditions, the proposed SELO-GLM/BIC procedure consistently selects the true model. We perform simulation studies to evaluate the finite sample performance of the proposed methods. Our simulation studies show that the proposed SELO-GLM pr

SUBMITTER: Li Z 

PROVIDER: S-EPMC3600656 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature