Essentials of Statistical Inference

This textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference.

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.

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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