Essentials of Statistical Inference

This textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference. 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 contemporary material on computational topics with basic mathematical theory. Each chapter includes instructive problems.

Informationen zum Autor 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. Klappentext 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. Zusammenfassung 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. Inhaltsverzeichnis 1. Introduction; 2. Decision theory; 3. Bayesian methods; 4. Hypothesis testing; 5. Special models; 6. Sufficiency and completeness; 7. Two-sided tests and conditional inference; 8. Likelihood theory; 9. Higher-order theory; 10. Predictive inference; 11. Bootstrap methods.

Autorentext
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.

Klappentext
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.

Zusammenfassung
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.

Inhalt
1. Introduction; 2. Decision theory; 3. Bayesian methods; 4. Hypothesis testing; 5. Special models; 6. Sufficiency and completeness; 7. Two-sided tests and conditional inference; 8. Likelihood theory; 9. Higher-order theory; 10. Predictive inference; 11. Bootstrap methods.

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