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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. [ABSTRACT FROM AUTHOR]