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.
Recommended Citation
L. Huo et al., "Trace Pursuit Variable Selection for Multi-Population Data," Journal of Nonparametric Statistics, vol. 30, no. 2, pp. 430 - 447, Taylor & Francis, Apr 2018.
The definitive version is available at https://doi.org/10.1080/10485252.2018.1430364
Department(s)
Mathematics and Statistics
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
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].