Abstract
A regression with multi-dimensional responses is quite common nowadays in the so-called big data era. In such regression, to relieve the curse of dimension due to high-dimension of responses, the dimension reduction of predictors is essential in analysis. Sufficient dimension reduction provides effective tools for the reduction, but there are few sufficient dimension reduction methodologies for multivariate regression. To fill this gap, we newly propose two fused slice-based inverse regression methods. The proposed approaches are robust to the numbers of clusters or slices and improve the estimation results over existing methods by fusing many kernel matrices. Numerical studies are presented and are compared with existing methods. Real data analysis confirms practical usefulness of the proposed methods.
| Original language | English |
|---|---|
| Pages (from-to) | 267-279 |
| Number of pages | 13 |
| Journal | Communications for Statistical Applications and Methods |
| Volume | 28 |
| Issue number | 3 |
| 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 sliced inverse regression
- multivariate regression
- pooled approach
- sufficient dimension reduction