Statistical Inference

• Unique combination of topics combining traditional inference with recent developments.
• Many exercises at varying levels of difficulty.
• Rigorous approach to the topic.

Readership: Final year undergraduates and graduates in statistics.

Paul Garthwaite, Department of Statistics, Open University, UK, Ian Jolliffe, Professor of Statistics, University of Aberdeen, and Byron Jones, Director, Research Statistics Unit (UK), GlaxoSmithKline, Harlow, UK

"... 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

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