  Robust inference based on the minimization of statistical divergences has
proved to be a useful alternative to the classical maximum likelihood based
techniques. Recently Ghosh et al. (2013) proposed a general class of divergence
measures for robust statistical inference, named the S-Divergence Family. Ghosh
(2014) discussed its asymptotic properties for the discrete model of densities.
In the present paper, we develop the asymptotic properties of the proposed
minimum S-Divergence estimators under continuous models. Here we use the
Basu-Lindsay approach (1994) of smoothing the model densities that, unlike
previous approaches, avoids much of the complications of the kernel bandwidth
selection. Illustrations are presented to support the performance of the
resulting estimators both in terms of efficiency and robustness through
extensive simulation studies and real data examples.
