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In den WarenkorbHardcover. Zustand: New. . . . . 8vo, hardcover. No dj. NEW. No wear inside or out: crisp, clean - pristine.492 pp. Mathematics, Probability, 1584886005.
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In den WarenkorbHardcover. Zustand: Fine. 1st Edition. No Gift Inscription Or Remarks Inside, 492 Pages With The Index. In Fine Condition. Hardcover. 2006. Pictorial Boards.books are NOT signed. We will state signed at the description section. we confirm they are signed via email or stated in the description box. - Specializing in academic, collectiblle and historically significant, providing the utmost quality and customer service satisfaction. For any questions feel free to email us.
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Verlag: Taylor & Francis Inc Okt 2005, 2005
ISBN 10: 1584886005 ISBN 13: 9781584886006
Sprache: Englisch
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In den WarenkorbBuch. Zustand: Neu. Neuware - 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.
Verlag: Taylor & Francis, Chapman And Hall/CRC, 2020
ISBN 10: 0367578018 ISBN 13: 9780367578015
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, presenting the interesting possibility of introducing alternative test statistics to classical ones like Wald, Rao, and likelihood ratio. Each chapter concludes with exercises that clarify the theoretical results and present additional results that complement the main discussions.Clear, comprehensive, and logically developed, this book offers a unique opportunity to gain not only a new perspective on some standard statistics problems, but the tools to put it into practice. 512 pp. Englisch.
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Leandro PardoThe idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical.
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In den WarenkorbTaschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, presenting the interesting possibility of introducing alternative test statistics to classical ones like Wald, Rao, and likelihood ratio. Each chapter concludes with exercises that clarify the theoretical results and present additional results that complement the main discussions.Clear, comprehensive, and logically developed, this book offers a unique opportunity to gain not only a new perspective on some standard statistics problems, but the tools to put it into practice.
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In den WarenkorbZustand: New. pp. xx + 492 Illus. This item is printed on demand.
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In den WarenkorbZustand: New. PRINT ON DEMAND pp. xx + 492.
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In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Leandro PardoThe idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical.