Essentials of Statistical Inference

Description

   

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.

- First text to synthesise basic mathematical theory with material on such modern computational topics as MCMC and bootstrap
- Covers main approaches to statistical inference - Bayesian, frequentist, Fisherian - with emphasis on the contrasts between them
- Written in an engaging style that emphasises concepts rather than dry mathematical detail, while maintaining appropriate levels of rigour



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