  This paper derives a new family of estimators, namely the minimum density
power divergence estimators, as a robust generalization of the maximum
likelihood estimator for the polytomous logistic regression model. Based on
these estimators, a family of Wald-type test statistics for linear hypotheses
is introduced. Robustness properties of both the proposed estimators and the
test statistics are theoretically studied through the classical influence
function analysis. Appropriate real life examples are presented to justify the
requirement of suitable robust statistical procedures in place of the
likelihood based inference for the polytomous logistic regression model. The
validity of the theoretical results established in the paper are further
confirmed empirically through suitable simulation studies. Finally, an approach
for the data-driven selection of the robustness tuning parameter is proposed
with empirical justifications.
