New Developments in Statistical Information Theory Based on Entropy and Divergence Measures

<p>This book presents new and original research in Statistical Information Theory based on minimum divergence estimators and test statistics from a theoretical and applied point of view for different statistical problems with special emphasis on efficiency and robustness. Divergence statistics based on maximum likelihood estimators as well as Wald’s statistics likelihood ratio statistics and Rao’s score statistics share several optimum asymptotic properties but are highly non-robust in cases of model misspecification under the presence of outlying observations. It is well-known that a small deviation from the underlying assumptions on the model can have drastic effect on the performance of these classical tests. Specifically this book presents a robust version of the classical Wald statistical test for testing simple and composite null hypotheses for general parametric models based on minimum divergence estimators.</p>