Title: Improvements in the Small Sample Efficiency of the Minimum $S$-Divergence Estimators under Discrete Models,
Abstract: This paper considers the problem of inliers and empty cells and the resulting
issue of relative inefficiency in estimation under pure samples from a discrete
population when the sample size is small. Many minimum divergence estimators in
the $S$-divergence family, although possessing very strong outlier stability
properties, often have very poor small sample efficiency in the presence of
inliers and some are not even defined in the presence of a single empty cell;
this limits the practical applicability of these estimators, in spite of their
otherwise sound robustness properties and high asymptotic efficiency. Here, we
will study a penalized version of the $S$-divergences such that the resulting
minimum divergence estimators are free from these issues without altering their
robustness properties and asymptotic efficiencies. We will give a general proof
for the asymptotic properties of these minimum penalized $S$-divergence
estimators. This provides a significant addition to the literature as the
asymptotics of penalized divergences which are not finitely defined are
currently unavailable in the literature. The small sample advantages of the
minimum penalized $S$-divergence estimators are examined through an extensive
simulation study and some empirical suggestions regarding the choice of the
relevant underlying tuning parameters are also provided.