TY - JOUR
T1 - Multiple-population shrinkage estimation via sliced inverse regression
AU - Wang, Tao
AU - Wen, Xuerong Meggie
AU - ZHU, Lixing
N1 - Publisher Copyright:
© 2015, Springer Science+Business Media New York.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - The problem of dimension reduction in multiple regressions is investigated in this paper, in which data are from several populations that share the same variables. Assuming that the set of relevant predictors is the same across the regressions, a joint estimation and selection method is proposed, aiming to preserve the common structure, while allowing for population-specific characteristics. The new approach is based upon the relationship between sliced inverse regression and multiple linear regression, and is achieved through the lasso shrinkage penalty. A fast alternating algorithm is developed to solve the corresponding optimization problem. The performance of the proposed method is illustrated through simulated and real data examples.
AB - The problem of dimension reduction in multiple regressions is investigated in this paper, in which data are from several populations that share the same variables. Assuming that the set of relevant predictors is the same across the regressions, a joint estimation and selection method is proposed, aiming to preserve the common structure, while allowing for population-specific characteristics. The new approach is based upon the relationship between sliced inverse regression and multiple linear regression, and is achieved through the lasso shrinkage penalty. A fast alternating algorithm is developed to solve the corresponding optimization problem. The performance of the proposed method is illustrated through simulated and real data examples.
KW - Joint sparsity
KW - Multiple regressions
KW - Sliced inverse regression
KW - Sufficient dimension reduction
UR - http://www.scopus.com/inward/record.url?scp=84947726550&partnerID=8YFLogxK
U2 - 10.1007/s11222-015-9609-y
DO - 10.1007/s11222-015-9609-y
M3 - Journal article
AN - SCOPUS:84947726550
SN - 0960-3174
VL - 27
SP - 103
EP - 114
JO - Statistics and Computing
JF - Statistics and Computing
IS - 1
ER -