Statistical Inference (Relié)

Adopting a broad view, Statistical Inference concentrates on what various techniques do, with mathematical proofs 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, decision theory and non-parametric and robust inference.The increasingly important topics of computationally intensive methods and generalized linear models are also included.
In this new edition, the material on recent developments has been updated, new sections on local likelihood, estimating equations, and fiducial intervals have been added ; and material on Gibbs sampling, tests based on ranks, bootstrap techniques, and generalized linear models has been revised substantially and expanded. Additional exercises are included in most chapters. Statistical Inference is designed for use on final year undergraduate or postgraduate courses.
It will also be useful to the working statistician as a reference for the basic ideas in statistical inference.