Feature Selection for Varying Coefficient Models With Ultrahigh Dimensional Covariates.

ABSTRACT: This paper is concerned with feature screening and variable selection for varying coefficient models with ultrahigh dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency. To enhance the finite sample performance of the proposed procedure, we further develop an iterative feature screening procedure. Monte Carlo simulation studies were conducted to examine the performance of the proposed procedures. In practice, we advocate a two-stage approach for varying coefficient models. The two stage approach consists of (a) reducing the ultrahigh dimensionality by using the pro

SUBMITTER: Liu J 

PROVIDER: S-EPMC3963210 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature