Fused sliced inverse regression in survival analysis

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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.

Original languageEnglish
Pages (from-to)533-541
Number of pages9
JournalCommunications for Statistical Applications and Methods
Issue number5
StatePublished - 1 Sep 2017


  • Bivariate slicing
  • Fused sliced inverse regression
  • Sufficient dimension reduction
  • Survival analysis


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