Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Fused sliced inverse regression in survival analysis

  • Yoo, Jae Keun (Department of Statistics, Ewha Womans University)
  • Received : 2017.07.21
  • Accepted : 2017.09.14
  • Published : 2017.09.30
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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.

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References

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