  Density-based minimum divergence procedures represent popular techniques in
parametric statistical inference. They combine strong robustness properties
with high (sometimes full) asymptotic efficiency. Among density-based minimum
distance procedures, the methods based on the Bregman-divergence have the
attractive property that the empirical formulation of the divergence does not
require the use of any non-parametric smoothing technique such as kernel
density estimation. The methods based on the density power divergence (DPD)
represent the current standard in this area of research. In this paper, we will
present a more generalized divergence that subsumes the DPD as a special case
and produces several new options providing better compromises between
robustness and efficiency.
