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Revised empirical likelihood

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Author Info

  • Grendar, Marian

    ()
    (Institute of Measurement Sciences SAS, Bratislava, Slovakia)

  • Judge, George G.

    ()
    (University of California, Berkeley. Dept of agricultural and resource economics)

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    Abstract

    Empirical Likelihood (EL) and other methods that operate within the Empirical Estimating Equations (E3) approach to estimation and inference are challenged by the Empty Set Problem (ESP). ESP concerns the possibility that a model set, which is data-dependent, may be empty for some data sets. To avoid ESP we return from E3 back to the Estimating Equations, and explore the Bayesian infinite-dimensional Maximum A-posteriori Probability (MAP) method. The Bayesian MAP with Dirichlet prior motivates a Revised EL (ReEL) method. ReEL i) avoids ESP as well as the convex hull restriction, ii) attains the same basic asymptotic properties as EL, and iii) its computation complexity is comparable to that of EL.

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    Bibliographic Info

    Paper provided by University of California at Berkeley, Department of Agricultural and Resource Economics and Policy in its series CUDARE Working Paper Series with number 1106.

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    Length: 17 pages
    Date of creation: Jul 2010
    Date of revision:
    Handle: RePEc:are:cudare:1106

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    Related research

    Keywords: empirical estimating equations; generalized minimum contrast; empirical likelihood; generalized empirical likelihood; empty set problem; convex hull restriction; estimating equations; maximum aposteriori probability;

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