Basis-Adaptive Selection Algorithm in dr-package
- 주제(기타) Computer Science, Interdisciplinary Applications; Statistics & Probability
- 설명문(일반) [Yoo, Jae Keun] Ewha Womans Univ, Dept Stat, Seoul 03760, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 R FOUNDATION STATISTICAL COMPUTING
- 발행년도 2018
- URI http://www.dcollection.net/handler/ewha/000000160175
- 본문언어 영어
초록/요약
Sufficient dimension reduction (SDR) turns out to be a useful dimension reduction tool in high-dimensional regression analysis. Weisberg (2002) developed the dr-package to implement the four most popular SDR methods. However, the package does not provide any clear guidelines as to which method should be used given a data. Since the four methods may provide dramatically different dimension reduction results, the selection in the dr-package is problematic for statistical practitioners. In this paper, a basis-adaptive selection algorithm is developed in order to relieve this issue. The basic idea is to select an SDR method that provides the highest correlation between the basis estimates obtained by the four classical SDR methods. A real data example and numerical studies confirm the practical usefulness of the developed algorithm.
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