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Groupwise sufficient dimension reduction via conditional distance clustering

Author

Listed:
  • 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. 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.
    2. Li, Lexin, 2009. "Exploiting predictor domain information in sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2665-2672, May.
    3. Li, Bing & Wang, Shaoli, 2007. "On Directional Regression for Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 997-1008, September.
    4. 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.
    5. 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.
    6. Li, Lexin & Li, Bing & Zhu, Li-Xing, 2010. "Groupwise Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1188-1201.
    7. 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.
    8. Yin, Xiangrong & Li, Bing & Cook, R. Dennis, 2008. "Successive direction extraction for estimating the central subspace in a multiple-index regression," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1733-1757, September.
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