Synopses & Reviews
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.
Review
"This is a delightful book! It gives a well-written exposure to inference issues in statistics, very suitable for a first-year graduate course...The authors present the material in a very good pedagogical manner. The examples are excellent, and the exercises are very instructive...very much up to date and includes recent developments in the field."
MAA Reviews"This is a solid book, ideal for advanced classes in the mathematical justification for statistical inference."
Journal of Recreational Mathematics"I wish that I had had such a textbook during my student days...this new book presents the core ideas of statistical inference in the unifying framework of decision theory and includes a fruitful discussion of the different foundational standpoints (Bayesian, Fisherian and frequentist)...[it is] sufficiently precise to satisfy a mathematician and yet omitting too much technical detail that could hide the core of the ideas. Carefully selected examples from a rainbow of application areas such as baseball, coal-mining disasters or gene expression data make it even more enjoyable to read...this book is a very nice graduate level textbook."
Journal of the Royal Statistical Society"[T]his book gives a clear and comprehensive account of the basic elements of statistical theory. It should make a good text for an advanced course on statistical inference...Students will find it informative and challenging."
ISI Short Book Reviews
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.
Synopsis
Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, this engaging textbook gives 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.
Synopsis
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.
Synopsis
Concise account of main approaches; first textbook to synthesize modern computation with basic theory.
Table of Contents
Preface; Introduction; 1. Decision theory; 2. Bayesian methods; 3. Hypothesis testing; 4. Special models; 5. Sufficiency and completeness; 6. Two-sided tests and conditional inference; 7. Likelihood theory; 8. Higher-order theory; 9. Predictive inference; 10. Bootstrap methods; References; Index.