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Statistics and Computing

, Volume 27, Issue 1, pp 103–114 | Cite as

Multiple-population shrinkage estimation via sliced inverse regression

  • Tao Wang
  • Xuerong Meggie Wen
  • Lixing ZhuEmail author
Article

Abstract

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.

Keywords

Joint sparsity Multiple regressions Sliced inverse regression Sufficient dimension reduction 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of BiostatisticsYale UniversityNew HavenUSA
  2. 2.Department of Mathematics and StatisticsMissouri University of Science and TechnologyRollaUSA
  3. 3.Department of MathematicsHong Kong Baptist UniversityKowloon TongHong Kong
  4. 4.School of StatisticsBeijing Normal UniversityBeijingChina

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