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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제29권 제5호
- 발행연도
- 2022.9
- 수록면
- 615 - 627 (13page)
이용수
초록· 키워드
오류제보하기
Principal Fitted Component (PFC) is a semi-parametric sufficient dimension reduction (SDR) method, which is originally proposed in Cook (2007). According to Cook (2007), the PFC has a connection with other usual non-parametric SDR methods. The connection is limited to sliced inverse regression (Li, 1991) and ordinary least squares. Since there is no direct comparison between the two approaches in various forward regressions up to date, a practical guidance between the two approaches is necessary for usual statistical practitioners. To fill this practical necessity, in this paper, we newly derive a connection of the PFC to covariance methods (Yin and Cook, 2002), which is one of the most popular SDR methods. Also, intensive numerical studies have done closely to examine and compare the estimation performances of the semi- and non-parametric SDR methods for various forward regressions. The founding from the numerical studies are confirmed in a real data example.
목차
Abstract
1. Introduction
2. Review: Semi-parametric inverse model
3. Connecting to covariance methods and sliced inverse regression
4. Numerical examples
5. Real data example - wheat data
6. Discussion
References
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UCI(KEPA) : I410-ECN-0101-2023-310-001433139