
Statistical Inference
Oxford University Press
2nd Edition
Published on 20. June 2002
Book
Hardback
342 pages
978-0-19-857226-8 (ISBN)
Description
Adopting a broad view of statistical inference, this text concentrates on what various techniques do, with mathematical proofs kept to a minimum. The approach is rigorous, but will be accessible to final year undergraduates. Classical approaches to point estimation, hypothesis testing and interval estimation are all covered thoroughly, with recent developments outlined. Separate chapters are devoted to Bayesian inference, to decision theory and to non-parametric and robust inference. The increasingly important topics of computationally intensive methods and generalised linear models are also included. In this edition, the material on recent developments has been updated, and additional exercises are included in most chapters.
Reviews / Votes
... clearly structured and reasonably compact ... should be useful for reference purposes. There is a carefully chosen bibliography and, whilst the subject matter is quite advanced, it is coherently argued throughout and can be thoroughly recommended. * The Mathematical Gazette * This book is easy to read and is designed to be an advanced level textbook for senior undergraduate students ... a useful, comprehensive reference for practising statisticians. * Zentralblatt MATH *More details
Edition
2. Auflage
Language
English
Place of publication
Oxford
United Kingdom
Target group
Professional and scholarly
Illustrations
numerous tables and figures
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 23 mm
Weight
678 gr
ISBN-13
978-0-19-857226-8 (9780198572268)
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Schweitzer Classification
Persons
Author
, Department of Statistics, Open University, UK
, Professor of Statistics, University of Aberdeen
, Director, Research Statistics Unit (UK), GlaxoSmithKline, Harlow, UK
Content
1. Introduction ; 2. Properties of estimators ; 3. Maximum likelihood and other methods of estimation ; 4. Hypothesis testing ; 5. Interval estimation ; 6. The decision theory approach to inference ; 7. Bayesian inference ; 8. Non-parametric and robust inference ; 9. Computationally intensive methods ; 10. Generalised linear models