  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.
