Profiled Forward Regression for Ultrahigh Dimensional Variable Screening in Semiparametric Partially Linear Models

26 Pages Posted: 24 Jan 2011

See all articles by Hua Liang

Hua Liang

affiliation not provided to SSRN

Hansheng Wang

Peking University - Guanghua School of Management

Chih-Ling Tsai

University of California, Davis - Graduate School of Management

Date Written: January 23, 2011

Abstract

In partially linear model selection, we develop a profiled forward regression (PFR) algorithm for ultrahigh dimensional variable screening. The PFR algorithm effectively combines the ideas of nonparametric profiling and forward regression. This allows us to obtain a uniform bound for the absolute difference between the profiled predictors and their estimators. Based on this important finding, we are able to show that the PFR algorithm discovers all relevant variables within a few fairly short steps. Numerical studies are presented to illustrate the performance of the proposed method.

Keywords: Forward Regression, Partially Linear Model, Profiled Forward Regression, Screening Consistency, Ultrahigh Dimensional Predictor

JEL Classification: C10, C13

Suggested Citation

Liang, Hua and Wang, Hansheng and Tsai, Chih-Ling, Profiled Forward Regression for Ultrahigh Dimensional Variable Screening in Semiparametric Partially Linear Models (January 23, 2011). Available at SSRN: https://ssrn.com/abstract=1746315 or http://dx.doi.org/10.2139/ssrn.1746315

Hua Liang

affiliation not provided to SSRN ( email )

Hansheng Wang (Contact Author)

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

HOME PAGE: http://hansheng.gsm.pku.edu.cn

Chih-Ling Tsai

University of California, Davis - Graduate School of Management ( email )

One Shields Avenue
Davis, CA 95616
United States

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