High-dimensional regression analysis with treatment comparisons

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• 作者：Heng-Hui Lue (1)
  Bing-Ran You (1)
  
• 关键词：Dimension reduction ; Nonparametric curve fitting ; Principal components analysis ; Shrinkage sparse estimator ; Sliced inverse regression ; Treatment effect
• 刊名：Computational Statistics
• 出版年：2013
• 出版时间：June 2013
• 年：2013
• 卷：28
• 期：3
• 页码：1299-1317
• 全文大小：389KB
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• 作者单位：Heng-Hui Lue (1)
  Bing-Ran You (1)
  
  1. Department of Statistics, Tunghai University, Taichung, Taiwan, ROC
  
• ISSN：1613-9658

文摘

We consider the treatment comparison problem in a general high-dimensional regression setting. In this article, we propose a nonparametric estimation approach based on partial sliced inverse regression (SIR) (Chiaromonte et al. in Ann Stat 30:475-97, 2002) and an extension of partial inverse mean matching (Carroll and Li in Stat Sin 5:667-88, 1995) without requiring a prespecified parametric model. A sparse estimation strategy is incorporated in our approach to enhance the interpretation of variable selection. Several simulation examples are used to compare our method with SIR and principal components analysis. Illustrative applications to two real datasets are also presented.