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Robust Z-Estimators for Semiparametric Moment Condition Models.

作者信息

Toma Aida

机构信息

Department of Applied Mathematics, Bucharest University of Economic Studies, 010374 Bucharest, Romania.

"Gheorghe Mihoc-Caius Iacob" Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, 050711 Bucharest, Romania.

出版信息

Entropy (Basel). 2023 Jun 30;25(7):1013. doi: 10.3390/e25071013.

Abstract

In the present paper, we introduce a class of robust Z-estimators for moment condition models. These new estimators can be seen as robust alternatives for the minimum empirical divergence estimators. By using the multidimensional Huber function, we first define robust estimators of the element that realizes the supremum in the dual form of the divergence. A linear relationship between the influence function of a minimum empirical divergence estimator and the influence function of the estimator of the element that realizes the supremum in the dual form of the divergence led to the idea of defining new Z-estimators for the parameter of the model, by using robust estimators in the dual form of the divergence. The asymptotic properties of the proposed estimators were proven, including here the consistency and their asymptotic normality. Then, the influence functions of the estimators were derived, and their robustness is demonstrated.

摘要

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本文引用的文献

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Robust Procedures for Estimating and Testing in the Framework of Divergence Measures.
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2
Robust Regression with Density Power Divergence: Theory, Comparisons, and Data Analysis.
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