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Essentials of Statistical Inference


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Table of Contents

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

About the Author

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.


'I wish that I had such a textbook during my student days ... The text is written in a style that I sought for without too much success in my student days: 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. ... a very nice graduate level textbook ...' Journal of the Royal Statistical Society: Series A (Statistics in Society) '... gives a clear and comprehensive account of the basic elements of statistical theory ... a good text for an advanced course on statistical inference.' Publication of the International Statistical Institute 'The text presents the main concepts and results underlying different frameworks of inference, with particular emphasis on the contrasts among frequentist, Fisherian, and Bayesian approaches. It provides a depiction of basic material on these main approaches to inference, as well as more advanced material on recent developments in statistical theory, including higher-order likelihood inference, bootstrap methods, conditional inference, and predictive inference.' Zentralblatt MATH

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