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Metrization of Epi-Convergence: An Application to the Strong Consistency of M-Estimators

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Journal of Computational Analysis and Applications

Abstract

Strong consistency in the class of M-estimators is examined here as an application of epi-convergence, a functional convergence which is particularly suited for the study of convergence of the functions' minimizing values and arguments. Starting from a 1988 paper by J. Dupačova and R. Wets, which contains a thorough account of the relations between consistency and epi-convergence, a quantitative approach of the same topic is pursued here. Epi-convergence is compared with two definitions introduced in 1980 by one of the authors. The results are merged in order to define a distance between lower semicontinuous functions that is compatible with epi-convergence and bounds the distance between the minimizing arguments. These results applied to the statistical problem allow the definition of a bound of the distance between the estimator and the parameter.

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Dall'Aglio, M., Rachev, S.T. Metrization of Epi-Convergence: An Application to the Strong Consistency of M-Estimators. Journal of Computational Analysis and Applications 1, 63–86 (1999). https://doi.org/10.1023/A:1022618620244

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  • DOI: https://doi.org/10.1023/A:1022618620244

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