Statistical Inference Based on Divergence Measures (Statistics: A Series of Textbooks and Monographs)

Leandro Pardo

ISBN 10: 1584886005 / ISBN 13: 9781584886006

Edité par Chapman and Hall/CRC, 2005

A propos de cet article

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.

N° de réf. du vendeur A9420a

Détails bibliographiques

• Titre: Statistical Inference Based on Divergence Measures (Statistics: A Series of Textbooks and Monographs)
• Éditeur: Chapman and Hall/CRC
• Date de parution: 2005
• Langue: anglais
• ISBN à 10 chiffres: 1584886005
• ISBN à 13 chiffres: 9781584886006
• Reliure: Hardcover
• Édition: 1st Edition.
• État de l'article: Fine
• Poids: 2 onces
• Catalogues du vendeur:
  • HOBBIES & ANTIQUES, Art

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Leandro Pardo