Published by Chapman and Hall/CRC, 2005
ISBN 10: 1584886005 ISBN 13: 9781584886006
Language: English
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Published by Chapman and Hall/CRC, 2005
ISBN 10: 1584886005 ISBN 13: 9781584886006
Language: English
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Hardcover. Condition: Very Good+. Dust Jacket Condition: No Dust Jacket. First Edition. Light rubbing and toning overall. Interior pages clean and unmarked. A tight and very clean copy. Photos upon request. International shipping billed at cost.; Statistics: A Series Of Textbooks And Monographs; 8vo 8" - 9" tall; 512 pages.
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Published by Taylor & Francis Ltd, 2020
ISBN 10: 0367578018 ISBN 13: 9780367578015
Language: English
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Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584886005 ISBN 13: 9781584886006
Language: English
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Hardcover. Condition: New. . . . . 8vo, hardcover. No dj. NEW. No wear inside or out: crisp, clean - pristine.492 pp. Mathematics, Probability, 1584886005.
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Add to basketPaperback. Condition: Brand New. 512 pages. 9.21x6.14x1.30 inches. In Stock.
Published by Chapman and Hall/CRC, 2005
ISBN 10: 1584886005 ISBN 13: 9781584886006
Language: English
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Add to basketHardcover. Condition: 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.
Published by Taylor & Francis Group, 2005
ISBN 10: 1584886005 ISBN 13: 9781584886006
Language: English
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Published by Taylor & Francis, Chapman And Hall/CRC, 2020
ISBN 10: 0367578018 ISBN 13: 9780367578015
Language: English
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Add to basketTaschenbuch. Condition: 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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Add to basketCondition: 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.
Published by Chapman And Hall/CRC, 2020
ISBN 10: 0367578018 ISBN 13: 9780367578015
Language: English
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Add to basketTaschenbuch. Condition: 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.
Published by Taylor & Francis Group, 2005
ISBN 10: 1584886005 ISBN 13: 9781584886006
Language: English
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Add to basketCondition: New. pp. xx + 492 Illus. This item is printed on demand.
Published by Taylor & Francis Group, 2005
ISBN 10: 1584886005 ISBN 13: 9781584886006
Language: English
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Add to basketCondition: New. PRINT ON DEMAND pp. xx + 492.
Seller: moluna, Greven, Germany
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Add to basketCondition: 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.