Statistical Inference [Hardcover]

Adopting a broad view of statistical inference, the text concentrates on what various techniques do, with mathematical proof kept to a minimum. The approach is rigorous but 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 generalized linear models are also included. In this edition, the material on recent developments has been updated, and additional exercises are included in most chapters.

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