Essentials of Statistical Inference

Erscheinungsjahr 2011
ISBN: 978-0-521-83971-6
Verlag: Cambridge University Press

Buch, Englisch, Band 16, 236 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 647 g

Reihe: Cambridge Series in Statistical and Probabilistic Mathematics

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.

Autoren/Hrsg.

Young, G. A.

Smith, R. L.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik

Inhaltsverzeichnis

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.

Über Autor(innen)

Smith, R. L.
R. L. Smith is Mark L. Reed Distinguished Professor of Statistics at the University of North Carolina, Chapel Hill.

Young, G. A.
G. A. Young is Professor of Statistics at Imperial College London.