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.
| Original language | English |
|---|---|
| Pages (from-to) | 533-541 |
| Number of pages | 9 |
| Journal | Communications for Statistical Applications and Methods |
| Volume | 24 |
| Issue number | 5 |
| DOIs | |
| State | Published - 1 Sep 2017 |
Bibliographical note
Funding Information:The authors are grateful to two reviewers and Associate Editor for insightful comments to improve the paper. For the author Jae Keun Yoo, this work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education (NRF-2014R1A2A1A11049389 and 2009-0093827).
Publisher Copyright:
© 2017 The Korean Statistical Society, and Korean International Statistical Society.
Keywords
- Bivariate slicing
- Fused sliced inverse regression
- Sufficient dimension reduction
- Survival analysis