Sparse Regression by Projection and Sparse Discriminant Analysis.

ABSTRACT: Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection

SUBMITTER: Qi X 

PROVIDER: S-EPMC4560121 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature