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Robust Empirical Likelihood

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Geometric Science of Information (GSI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12829))

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Abstract

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.

This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI – UEFISCDI, project number PN-III-P4-ID-PCE-2020-1112, within PNCDI III.

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Notes

  1. 1.

    The convex conjugate, called also Fenchel-Legendre transform, of \(\varphi \), is the function defined on \(\mathbb {R}\) by \(\psi (u) := \sup _{x\in \mathbb {R}}\{u x - \varphi (x) \} , \, \forall u\in \mathbb {R}\).

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Author information

Authors and Affiliations

  1. Université de Reims Champagne-Ardenne, Laboratoire de Mathématiques de Reims (LMR) - CNRS UMR 9008, U.F.R. Sciences Exactes et Naturelles Moulin de la Housse, BP 1039, 51687, Reims Cedex 2, France

    Amor Keziou

  2. Department of Applied Mathematics, Bucharest University of Economic Studies, Piaţa Romană No. 6, Bucharest, Romania

    Aida Toma

  3. Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, Bucharest, Romania

    Aida Toma

Authors
  1. Amor Keziou
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  2. Aida Toma
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Corresponding author

Correspondence to Amor Keziou .

Editor information

Editors and Affiliations

  1. Sony Computer Science Laboratories Inc., Tokyo, Japan

    Frank Nielsen

  2. THALES Land & Air Systems, Meudon, France

    Frédéric Barbaresco

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Keziou, A., Toma, A. (2021). Robust Empirical Likelihood. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2021. Lecture Notes in Computer Science(), vol 12829. Springer, Cham. https://doi.org/10.1007/978-3-030-80209-7_90

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  • DOI: https://doi.org/10.1007/978-3-030-80209-7_90

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80208-0

  • Online ISBN: 978-3-030-80209-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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