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.

Review

... 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)

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