Paperback. Aimed at advanced undergraduate and graduate students in mathematics and related disciplines, this book presents the concepts and results underlying the Bayesian, frequentist and Fisherian approaches, with particular emphasis on the contrasts between them. Computational ideas are explained, as well as basic mathematical theory. Written in a lucid and informal style, this concise text provides both basic material on the main approaches to inference, as well as more advanced material on developments in statistical theory, including: material on Bayesian computation, such as MCMC, higher-order likelihood theory, predictive inference, bootstrap methods and conditional inference. It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject. Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality. Each chapter ends with a set of accessible problems. Written for advanced undergraduates and graduate students in mathematics and related disciplines, this book explains the main approaches to statistical inference, with particular emphasis on the contrasts between them. It is the first textbook to synthesize material on computational topics with basic mathematical theory. Each chapter includes instructive problems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Bibliographic Details

Title: Essentials of Statistical Inference (Paperback)
Publisher: Cambridge University Press, Cambridge
Publication Date: 2010
Language: English
ISBN 10: 0521548667
ISBN 13: 9780521548663
Binding: Paperback
Condition: new

About this title

Synopsis

This textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference, with particular emphasis on the contrasts between them. Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, it covers basic mathematical theory as well as more advanced material, including such contemporary topics as Bayesian computation, higher-order likelihood theory, predictive inference, bootstrap methods, and conditional inference.

About the Authors

G. A. Young is Professor of Statistics at Imperial College London.

R. L. Smith is Mark L. Reed Distinguished Professor of Statistics at the University of North Carolina, Chapel Hill.

"About this title" may belong to another edition of this title.

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