Multiple-Population Shrinkage Estimation Via Sliced Inverse Regression
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.
T. Wang et al., "Multiple-Population Shrinkage Estimation Via Sliced Inverse Regression," Statistics and Computing, vol. 27, no. 1, pp. 103-114, Springer Verlag, Jan 2017.
The definitive version is available at https://doi.org/10.1007/s11222-015-9609-y
Mathematics and Statistics
Keywords and Phrases
Joint sparsity; Multiple regressions; Sliced inverse regression; Sufficient dimension reduction
International Standard Serial Number (ISSN)
Article - Journal
© 2017 Springer Verlag, All rights reserved.
01 Jan 2017