VARIABLE SELECTION FOR REGRESSION MODELS WITH MISSING DATA.

ABSTRACT: We consider the variable selection problem for a class of statistical models with missing data, including missing covariate and/or response data. We investigate the smoothly clipped absolute deviation penalty (SCAD) and adaptive LASSO and propose a unified model selection and estimation procedure for use in the presence of missing data. We develop a computationally attractive algorithm for simultaneously optimizing the penalized likelihood function and estimating the penalty parameters. Particularly, we propose to use a model selection criterion, called the IC(Q) statistic, for selecting the penalty parameters. We show that the variable selection procedure based on IC(Q) automatically and consistently selects the important covariates and leads to efficient estimates with oracle properties.

SUBMITTER: Garcia RI 

PROVIDER: S-EPMC2844735 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature