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General directional regression

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  • Yu, Zhou
  • Dong, Yuexiao
  • Huang, Mian

Abstract

Directional regression is an effective sufficient dimension reduction method which implicitly synthesizes the first two conditional moments. In this paper, we extend directional regression to a general family of estimators via the notion of general empirical directions. Data-driven method is used to identify the optimal estimator within this family. Based on the proposed general directional regression estimators, we develop a new methodology for nonlinear dimension reduction. Improvement of general directional regression over classical directional regression is demonstrated via simulation studies and an empirical study with the wine recognition data.

Suggested Citation

  • Yu, Zhou & Dong, Yuexiao & Huang, Mian, 2014. "General directional regression," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 94-104.
    • Handle: RePEc:eee:jmvana:v:124:y:2014:i:c:p:94-104
    DOI: 10.1016/j.jmva.2013.10.016
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    References listed on IDEAS

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    1. Ye Z. & Weiss R.E., 2003. "Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 968-979, January.
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    5. 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.
    6. Yuexiao Dong & Bing Li, 2010. "Dimension reduction for non-elliptically distributed predictors: second-order methods," Biometrika, Biometrika Trust, vol. 97(2), pages 279-294.
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    Cited by:

    1. Dong, Yuexiao & Li, Zeda, 2024. "A note on marginal coordinate test in sufficient dimension reduction," Statistics & Probability Letters, Elsevier, vol. 204(C).

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