Robust Empirical Likelihood

被引：0

作者：

Keziou, Amor ^{[1
]}

Toma, Aida ^{[2
,3
]}

机构：

[1] Univ Reims, Lab Math Reims LMR CNRS UMR 9008, UFR Sci Exactes & Nat Moulin Housse, BP 1039, F-51687 Reims 2, France

[2] Bucharest Univ Econ Studies, Dept Appl Math, Piata Romana 6, Bucharest, Romania

[3] Romanian Acad, Gheorghe Mihoc Caius Iacob Inst Math Stat & Appl, Bucharest, Romania

来源：

GEOMETRIC SCIENCE OF INFORMATION (GSI 2021) | 2021年 / 12829卷

关键词：

Moment condition models; Robustness; Divergence measures; GENERALIZED-METHOD; SAMPLE PROPERTIES; MOMENTS; DIVERGENCES; GMM; ESTIMATORS; DUALITY;

D O I：

10.1007/978-3-030-80209-7_90

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present a robust version of the empirical likelihood estimator for semiparametric moment condition models. This estimator is obtained by minimizing the modified Kullback-Leibler divergence, in its dual form, using truncated orthogonality functions. Some asymptotic properties regarding the limit laws of the estimators are stated.

引用

页码：841 / 848

页数：8

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