Groupwise sufficient dimension reduction via conditional distance clustering

Xinyi Xu and Jingxiao Zhang ()
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Xinyi Xu: Renmin University of China
Jingxiao Zhang: Renmin University of China

Metrika: International Journal for Theoretical and Applied Statistics, 2020, vol. 83, issue 2, No 4, 217-242

Abstract: Abstract It becomes increasingly common to incorporate the predictors’ grouping knowledge into dimension reduction techniques. In this article, we establish a complete framework named groupwise sufficient dimension reduction via conditional distance clustering, when the grouping information is unknown. We introduce a simple-type conditional dependence measurement and a corresponding conditional independence test. A clustering procedure based on the measurement and test is constructed to detect the suitable group structure. Finally we conduct sufficient dimension reduction under the obtained structure. Both simulations and a real data analysis demonstrate that the clustering strategy is effective, and the groupwise sufficient dimension reduction method is generally superior to the classical sufficient dimension reduction method.

Keywords: Sufficient dimension reduction; Group structure; Conditional independence; Conditional distance clustering (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00184-019-00732-7

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