Title

Trace Pursuit Variable Selection for Multi-Population Data

Abstract

Variable selection is a very important tool when dealing with high dimensional data. However, most popular variable selection methods are model based, which might provide misleading results when the model assumption is not satisfied. Sufficient dimension reduction provides a general framework for model-free variable selection methods. In this paper, we propose a model-free variable selection method via sufficient dimension reduction, which incorporates the grouping information into the selection procedure for multi-population data. Theoretical properties of our selection methods are also discussed. Simulation studies suggest that our method greatly outperforms those ignoring the grouping information.

Department(s)

Mathematics and Statistics

Comments

This work was supported by National Natural Science Foundation of China [1157111], the program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, the Shanghai Rising-Star Program [16QA1401700], and the 111 project [B14019].

Keywords and Phrases

Partial central subspace; Sufficient dimension reduction; Trace pursuit; Variable selection

International Standard Serial Number (ISSN)

1048-5252; 1029-0311

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 American Statistical Association and Taylor & Francis, All rights reserved.

Publication Date

01 Apr 2018

Share

 
COinS