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Groupwise sufficient dimension reduction via conditional distance clustering

Author

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  • Xinyi Xu

    (Renmin University of China)

  • Jingxiao Zhang

    (Renmin University of China)

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.

Suggested Citation

  • Xinyi Xu & Jingxiao Zhang, 2020. "Groupwise sufficient dimension reduction via conditional distance clustering," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(2), pages 217-242, February.
    • Handle: RePEc:spr:metrik:v:83:y:2020:i:2:d:10.1007_s00184-019-00732-7
    DOI: 10.1007/s00184-019-00732-7
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    References listed on IDEAS

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    1. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
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    4. Xueqin Wang & Wenliang Pan & Wenhao Hu & Yuan Tian & Heping Zhang, 2015. "Conditional Distance Correlation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1726-1734, December.
    5. Zifang Guo & Lexin Li & Wenbin Lu & Bing Li, 2015. "Groupwise Dimension Reduction via Envelope Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1515-1527, December.
    6. Cook, R. Dennis & Forzani, Liliana, 2009. "Likelihood-Based Sufficient Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 197-208.
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