Sufficient dimension reduction through informative predictor subspace. Issue 5 (2nd September 2016)

• Record Type: Journal Article
• Authors: Yoo, Jae Keun
• Is Part Of: Statistics. Volume 50:Issue 5(2016)
• Page Start: 1086
• Page End: 1099
• Publication Date: 2016-09-02
• Subjects: central subspace -- informative predictor subspace -- linearity condition -- regression -- sufficient dimension reduction; 62G08 -- 62H05; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2016.1148151
• Languages: English
• ISSNs: 0233-1888

Abstract

Abstract : The purpose of this paper is to define the central informative predictor subspace to contain the central subspace and to develop methods for estimating the former subspace. Potential advantages of the proposed methods are no requirements of linearity, constant variance and coverage conditions in methodological developments. Therefore, the central informative predictor subspace gives us the benefit of restoring the central subspace exhaustively despite failing the conditions. Numerical studies confirm the theories, and real data analyses are presented.