A Robust Version of the Empirical Likelihood Estimator

被引：1
|
作者：
Keziou, Amor [1 ,2 ]
Toma, Aida [3 ,4 ]
机构：
[1] CNRS, UMR9008, Lab Math Reims, BP 1039, F-51687 Reims, France
[2] Univ Reims, UFR SEN, BP 1039, F-51687 Reims, France
[3] Bucharest Univ Econ Studies, Dept Appl Math, Piata Romana 6, Bucharest 010374, Romania
[4] Romanian Acad, Gheorghe Mihoc Caius Iacob Inst Math Stat & Appl, Calea 13 Septembrie 13, Bucharest 050711, Romania
来源：
MATHEMATICS | 2021年 / 9卷 / 08期
关键词：
moment condition models; estimation; robustness; empirical likelihood;
D O I：
10.3390/math9080829
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, we introduce 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. We prove the robustness and the consistency of the new estimator. The performance of the robust empirical likelihood estimator is illustrated through examples based on Monte Carlo simulations.
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页数：19
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