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  • Format: ePub

Organized in systematic way, Statistical Inference Based on Divergence Measures presents classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence with applications to multinomial and generation populations. On the basis of divergence measures, this book introduces minimum divergence estimators as well as divergence test statistics and compares them to the classical maximum likelihood estimator, chi-square test statistics, and the likelihood ratio test in different statistical problems. The text includes over 120…mehr

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Produktbeschreibung
Organized in systematic way, Statistical Inference Based on Divergence Measures presents classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence with applications to multinomial and generation populations. On the basis of divergence measures, this book introduces minimum divergence estimators as well as divergence test statistics and compares them to the classical maximum likelihood estimator, chi-square test statistics, and the likelihood ratio test in different statistical problems. The text includes over 120 exercises with solutions, making it ideal for students with a basic knowledge of statistical methods.

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Rezensionen
"There are a number of measures of divergence between distributions. Describing them properly requires a very mathematically well-written book, which the author here provides ... This book is a fine course text, and is beautifully produced. There are about four hundred references. Recommended."
-ISI Short Book Reviews

". . . suitable for a beginning graduate course on information theory based on statistical inference. This book will be a useful and important addition to the resources of practitioners and many others engaged information theory and statistics. Overall, this is an impressive book on information theory based statistical inference."

- Prasanna Sahoo, in Zentralblatt Math, 2008, Vol. 1120