Asymptotic Properties of Minimum S-Divergence Estimator for Discrete Models

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.Comment: Under review, 24 page

Testing Composite Null Hypothesis Based on SS-Divergences

We present a robust test for composite null hypothesis based on the general $S$-divergence family. This requires a non-trivial extension of the results of Ghosh et al.~(2015). We derive the asymptotic and theoretical robustness properties of the resulting test along with the properties of the minimum $S$-divergence estimators under parameter restrictions imposed by the null hypothesis. An illustration in the context of the normal model is also presented.Comment: 13 page