Robust estimating equation-based sufficient dimension reduction

Jingke Zhou, Wangli Xu, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)99-118
Number of pages20
JournalJournal of Multivariate Analysis
Publication statusPublished - 1 Feb 2015

Scopus Subject Areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Outliers
  • Robust conditional density estimation
  • Robust nonparametric regression
  • Robust sufficient dimension reduction
  • Semiparametric inference


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