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Asymptotic Properties of Minimum S-Divergence Estimator for Discrete Models

Abstract

Robust inference based on the minimization of statistical divergences has proved to be a useful alternative to the classical techniques based on maximum likelihood and related methods. Recently Ghosh et al. (2013b) proposed a general class of divergence measures, namely the S-Divergence Family and discussed its usefulness in robust parametric estimation through some numerical illustrations. In this present paper, we develop the asymptotic properties of the proposed minimum S-Divergence estimators under discrete models.

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Correspondence to Abhik Ghosh.

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This is part of the Ph.D. research work of the author which is ongoing under the supervision of Prof. Ayanendranath Basu at the Indian Statistical Institute

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Ghosh, A. Asymptotic Properties of Minimum S-Divergence Estimator for Discrete Models. Sankhya A 77, 380–407 (2015). https://doi.org/10.1007/s13171-014-0063-2

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  • DOI: https://doi.org/10.1007/s13171-014-0063-2

Keywords and phrases

  • S-Divergence
  • robustness
  • asymptotic normality

AMS (2000) subject classification

  • Primary 62F12
  • Secondary 62F35