发帖
雷达卡
AUTHORS:
G. A. Young, Imperial College of Science, Technology and Medicine, London
R. L. Smith, University of North Carolina, Chapel Hill
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.
• Very concise account of the fundamental core of statistical inference
• Gives a broad treatment of its subject, emphasizing both Bayesian and frequentist approaches
• Emphasizes computational techniques as well as basic theory
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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Essentials of Statistical Inference.pdf
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Essentials of Statistical Inference.zip
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