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Robust estimating equation-based sufficient dimension reduction

Author

Listed:
  • Zhou, Jingke
  • Xu, Wangli
  • Zhu, Lixing

Abstract

In this paper, from the estimating equation-based sufficient dimension reduction method in the literature, its robust version is proposed to alleviate the impact from outliers. To achieve this, a robust nonparametric regression estimator is suggested. The estimator is plugged in the estimating equation of the semiparametric sufficient dimension reduction to obtain robust estimator for the central subspace. The asymptotic properties and robustness of the estimator are investigated. Numerical simulation and real data analysis are conducted to examine the performance of the estimators.

Suggested Citation

  • Zhou, Jingke & Xu, Wangli & Zhu, Lixing, 2015. "Robust estimating equation-based sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 99-118.
    • Handle: RePEc:eee:jmvana:v:134:y:2015:i:c:p:99-118
    DOI: 10.1016/j.jmva.2014.10.006
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Jingke & Zhu, Lixing, 2016. "Principal minimax support vector machine for sufficient dimension reduction with contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 33-48.

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