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Multiple-population shrinkage estimation via sliced inverse regression

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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.

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Authors and Affiliations

  1. Department of Biostatistics, Yale University, New Haven, CT, 06520, USA

    Tao Wang

  2. Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO, 65409, USA

    Xuerong Meggie Wen

  3. Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Lixing Zhu

  4. School of Statistics, Beijing Normal University, Beijing, China

    Lixing Zhu

Authors
  1. Tao Wang
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  2. Xuerong Meggie Wen
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  3. Lixing Zhu
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Correspondence to Lixing Zhu.

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Wang, T., Wen, X.M. & Zhu, L. Multiple-population shrinkage estimation via sliced inverse regression. Stat Comput 27, 103–114 (2017). https://doi.org/10.1007/s11222-015-9609-y

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  • DOI: https://doi.org/10.1007/s11222-015-9609-y

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