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.

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Essentials of Statistical Inference

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