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
收藏
页码：841 / 848
页数：8
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