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Essentials of Statistical Inference (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 16) - Hardcover
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.
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Book Description
This engaging textbook introduces the key ideas behind drawing formal inferences from data. Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, it is a concise account of the main approaches to inference, with particular emphasis on the contrasts between them. It is the first textbook to synthesize contemporary material on computational topics with basic mathematical theory. Numerous extended examples apply formal inference techniques to real data, while historical commentary sketches the development of the subject. Each chapter ends with a set of accessible problems.
About the Author
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.
- PublisherCambridge University Press
- Publication date2005
- ISBN 10 0521839718
- ISBN 13 9780521839716
- BindingHardcover
- LanguageEnglish
- Number of pages236
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Essentials Of Statistical Inference (Cambridge Series In Statistical And Probabilistic Mathematics, Series Number 16)
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Hardcover. Condition: new. Hardcover. This engaging 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 in a concise treatment both basic mathematical theory and more advanced material, including such contemporary topics as Bayesian computation, 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. Some prior knowledge of probability is assumed, while some previous knowledge of the objectives and main approaches to statistical inference would be helpful but is not essential. This textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521839716
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