Abstract
Sufficient dimension reduction is useful dimension reduction tool in regression, and sliced inverse regression (Li, 1991) is one of the most popular sufficient dimension reduction methodologies. In spite of its popularity, it is known to be sensitive to the number of slices. To overcome this shortcoming, the so-called fused sliced inverse regression is proposed by Cook and Zhang (2014). Unfortunately, the two existing methods do not have the direction application to large p-small n regression, in which the dimension reduction is desperately needed. In this paper, we newly propose seeded sliced inverse regression and seeded fused sliced inverse regression to overcome this deficit by adopting iterative projection approach (Cook et al., 2007). Numerical studies are presented to study their asymptotic estimation behaviors, and real data analysis confirms their practical usefulness in high-dimensional data analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 205-215 |
| Number of pages | 11 |
| Journal | Communications for Statistical Applications and Methods |
| Volume | 28 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2021 |
Bibliographical note
Funding Information:For 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-2019R1F1A1050715).
Publisher Copyright:
©2021 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.
Keywords
- central subspace
- fused reduction
- inverse regression
- iterative projection
- large psmall n regression
- sufficient dimension reduction