Essentials of Statistical Inference (Cambridge Series in Statistical and Probabilistic Mathematics) 1st Edition by Young, G. A.; Smith, R. L. published by Cambridge University Press Paperback

Cambridge Series in Statistical and Probabilistic Mathematics #16

by G.A. Young

"Written in an informal style, this concise text provides both basic material on the main approaches to inference, as well as more advanced material on modern developments in statistical theory, contemporary material on Bayesian computation, such as MCMC, higher-order likelihood theory, predictive inference, bootstrap methods and conditional inference. It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject. Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality. Each chapter ends with a set of accessible problems." Based to a large extent on lectures given at the University of Cambridge over a number of years, the material has been polished by student feedback. Some prior knowledge of probability is assumed, while some previous knowledge of the objectives and main approaches to statistical inference would be helpful but is not essential.

Genres: Mathematics

Paperback

First published July 25, 2005

Reviews

[Lucille Nguyen · Rating 5 out of 5]

Readable overview of parametric statistical inference. Good for a review.