다변량회귀에서 주선택 반응변수 차원축소
Principal selected response reduction in multivariate regression
- 주제(키워드) 다변량 회귀분석 , 모형기반 차원축소 , 주 반응변수 차원축소 , 주적합 반응변수 차원축소 , 비구조 주적합 반응변수 차원축소 , model-based response reduction , multivariate regression , principal fitted response reduction , principal response reduction , unstructured principal fitted response reduction
- 주제(기타) 통계학
- 설명문(URI) https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002748218
- 등재 KCI등재
- 발행기관 한국통계학회
- 발행년도 2021
- URI http://www.dcollection.net/handler/ewha/000000183142
- 본문언어 한국어
초록/요약
Multivariate regression often appears in longitudinal or functional data analysis. Since multivariate regression involves multi-dimensional response variables, it is more strongly affected by the so-called curse of dimension that univariate regression. To overcome this issue, Yoo (2018) and Yoo (2019a) proposed three model-based response dimension reduction methodologies. According to various numerical studies in Yoo (2019a), the default method suggested in Yoo (2019a) is least sensitive to the simulated models, but it is not the best one. To release this issue, the paper proposes an selection algorithm by comparing the other two methods with the default one. This approach is called {\em principal selected response reduction}. Various simulation studies show that the proposed method provides more accurate estimation results than the default one by Yoo (2019a), and it confirms practical and empirical usefulness of the propose method over the default one by Yoo (2019a).
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