Sufficient dimension reduction through informative predictor subspace
- 주제(키워드) central subspace , informative predictor subspace , linearity condition , regression , sufficient dimension reduction
- 등재 SCIE, SCOPUS
- 발행기관 Taylor and Francis Ltd.
- 발행년도 2016
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000135494
- 본문언어 영어
- Published As http://dx.doi.org/10.1080/02331888.2016.1148151
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
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. © 2016 Taylor & Francis
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