Year: 2012
Type: article
Abstract: In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X(1), X(2), …, X(p) through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics.
Authors: Wenxuan Zhong, Tingting Zhang, Yu Zhu, Jun S. Liu
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Awards: Bayesian Partition Models for Detecting Influential and Interactive Variables, G2719944227, Collaborative Research: Integral Transform Methods for Sufficient Dimension Reduction in Regression, ATD Collaborative Research: Statistical Modeling of Short-Read Counts in RNA-Seq, G7411634957, G9000000456, G9000149212
OpenAlex ID: W1902723335