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. (2013) 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.

Under review, 24 pages

References in corpus (1)

• On the Robustness of a Divergence based Test of Simple Statistical Hypotheses

Cited by in corpus (6)

• The Logarithmic Super Divergence and Statistical Inference : Asymptotic Properties
• The Minimum S-Divergence Estimator under Continuous Models: The Basu-Lindsay Approach
• Influence Function Analysis of the Restricted Minimum Divergence Estimators : A General Form
• A New Family of Divergences Originating from Model Adequacy Tests and Application to Robust Statistical Inference
• Testing Composite Null Hypothesis Based on $S$-Divergences
• Improvements in the Small Sample Efficiency of the Minimum $S$-Divergence Estimators under Discrete Models