Essentials of statistical inference
Author(s)
Bibliographic Information
Essentials of statistical inference
（Cambridge series on statistical and probabilistic mathematics）
Cambridge University Press, 2005
- : hbk
Available at / 27 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references (p. [218]-222) and index
Description and Table of Contents
Description
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.
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.
by "Nielsen BookData"
