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

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, base...

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Main Author: Pardo, Leandro
Format: eBook
Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
Subjects:
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Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Summary: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.
Physical Description:1 electronic resource (344 p.)
ISBN:9783038979364
9783038979371