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A Robust Version of the Empirical Likelihood Estimator

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  • Amor Keziou

    (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
    These authors contributed equally to this work.)

  • Aida Toma

    (Department of Applied Mathematics, Bucharest University of Economic Studies, Piaţa Romană no. 6, 010374 Bucharest, Romania
    “Gheorghe Mihoc-Caius Iacob” Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, Calea 13 Septembrie no. 13, 050711 Bucharest, Romania
    These authors contributed equally to this work.)

Abstract

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.

Suggested Citation

  • Amor Keziou & Aida Toma, 2021. "A Robust Version of the Empirical Likelihood Estimator," Mathematics, MDPI, vol. 9(8), pages 1-19, April.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:829-:d:533560
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    References listed on IDEAS

    as
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