A Robust Version of the Empirical Likelihood Estimator

Mathematics

Aida Toma¹  Amor Keziou² 

[1] Department of Applied Mathematics, Bucharest University of Economic Studies, Piaţa Romană no. 6, 010374 Bucharest, Romania;Laboratoire de Mathématiques de Reims, UMR9008 CNRS et Université de Reims Champagne-Ardenne, UFR SEN, Moulin de la Housse, B.P. 1039, 51687 Reims, France;

关键词: moment condition models;    estimation;    robustness;    empirical likelihood;   

DOI  :  10.3390/math9080829

来源: DOAJ

【 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.

【 授权许可】

Unknown