Response dimension reduction: model-based approach
- 주제(키워드) Envelope , Grassmann manifold , multivariate regression , response dimension reduction , sufficient dimension reduction
- 주제(기타) Statistics & Probability
- 설명문(일반) [Yoo, Jae Keun] Ewha Womans Univ, Dept Stat, Seoul, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 TAYLOR & FRANCIS LTD
- 발행년도 2018
- URI http://www.dcollection.net/handler/ewha/000000151697
- 본문언어 영어
- Published As http://dx.doi.org/10.1080/02331888.2017.1410152
초록/요약
In this paper, a model-based approach to reduce the dimension of response variables in multivariate regression is newly proposed, following the existing context of the response dimension reduction developed by Yoo and Cook [Response dimension reduction for the conditional mean in multivariate regression. Comput Statist Data Anal. 2008;53:334-343]. The related dimension reduction subspace is estimated by maximum likelihood, assuming an additive error. In the new approach, the linearity condition, which is assumed for the methodological development in Yoo and Cook (2008), is understood through the covariance matrix of the random error. Numerical studies show potential advantages of the proposed approach over Yoo and Cook (2008). A real data example is presented for illustration.
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