Unstructured principal fitted response reduction in multivariate regression
- 주제(키워드) Model-based reduction , Multivariate regression , Response dimension reduction , Sufficient dimension reduction
- 등재 SCIE, SCOPUS, KCI등재
- 발행기관 Korean Statistical Society
- 발행년도 2019
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000162294
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.jkss.2019.02.001
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
In this paper, an unstructured principal fitted response reduction approach is proposed. The new approach is mainly different from two existing model-based approaches, because a required condition is assumed in a covariance matrix of the responses instead of that of a random error. Also, it is invariant under one of popular ways of standardizing responses with its sample covariance equal to the identity matrix. According to numerical studies, the proposed approach yields more robust estimation than the two existing methods, in the sense that its asymptotic performances are not severely sensitive to various situations. So, it can be recommended that the proposed method should be used as a default model-based method. © 2019 The Korean Statistical Society
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