TY - JOUR
T1 - Iterative projection of sliced inverse regression with fused approach
AU - Han, Hyoseon
AU - Cho, Youyoung
AU - Yoo, Jae Keun
N1 - 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.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - central subspace
KW - fused reduction
KW - inverse regression
KW - iterative projection
KW - large psmall n regression
KW - sufficient dimension reduction
UR - http://www.scopus.com/inward/record.url?scp=85104806606&partnerID=8YFLogxK
U2 - 10.29220/CSAM.2021.28.2.205
DO - 10.29220/CSAM.2021.28.2.205
M3 - Article
AN - SCOPUS:85104806606
SN - 2287-7843
VL - 28
SP - 205
EP - 215
JO - Communications for Statistical Applications and Methods
JF - Communications for Statistical Applications and Methods
IS - 2
ER -