  Statistical inference based on divergence measures have a long history.
Recently, Maji, Ghosh and Basu (2014) have introduced a general family of
divergences called the logarithmic super divergence (LSD) family. This family
acts as a superfamily for both of the logarithmic power divergence (LPD) family
(eg. Renyi, 1961) and the logarithmic density power divergence (LDPD)family
introduced by Jones et al. (2001). In this paper we describe the asymptotic
properties of the inference procedures resulting from this divergence in
discrete models. The properties are well supported by real data examples.
