Fused sliced inverse regression in survival analysis
- 주제(키워드) Bivariate slicing , Fused sliced inverse regression , Sufficient dimension reduction , Survival analysis
- 등재 SCOPUS, KCI등재
- 발행기관 Korean Statistical Society
- 발행년도 2017
- URI http://www.dcollection.net/handler/ewha/000000156431
- 본문언어 영어
- Published As http://dx.doi.org/10.5351/CSAM.2017.24.5.533
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Sufficient dimension reduction (SDR) replaces original p-dimensional predictors to a lower-dimensional linearly transformed predictor. The sliced inverse regression (SIR) has the longest and most popular history of SDR methodologies. The critical weakness of SIR is its known sensitive to the numbers of slices. Recently, a fused sliced inverse regression is developed to overcome this deficit, which combines SIR kernel matrices constructed from various choices of the number of slices. In this paper, the fused sliced inverse regression and SIR are compared to show that the former has a practical advantage in survival regression over the latter. Numerical studies confirm this and real data example is presented. © 2017 The Korean Statistical Society, and Korean International Statistical Society.
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