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Fused sliced inverse regression in survival analysis

Title
Fused sliced inverse regression in survival analysis
Authors
Yoo J.K.
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2017
Journal Title
Communications for Statistical Applications and Methods
ISSN
2287-7843JCR Link
Citation
Communications for Statistical Applications and Methods vol. 24, no. 5, pp. 533 - 541
Keywords
Bivariate slicingFused sliced inverse regressionSufficient dimension reductionSurvival analysis
Publisher
Korean Statistical Society
Indexed
SCOPUS; KCI scopus
Document Type
Article
Abstract
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.
DOI
10.5351/CSAM.2017.24.5.533
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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