In All Likelihood: Statistical Modelling and Inference Using Likelihood

by Yudi Pawitan

Based on a course in the theory of statistics this text concentrates on what can be achieved using the likelihood/Fisherian method of taking account of uncertainty when studying a statistical problem. It takes the concept ot the likelihood as providing the best methods for unifying the demands of statistical modelling and the theory of inference. Every likelihood concept is illustrated by realistic examples, which are not compromised by computational problems. Examples range from a simile comparison of two accident rates, to complex studies that require generalised linear or semiparametric modelling.

The emphasis is that the likelihood is not simply a device to produce an estimate, but an important tool for modelling. The book generally takes an informal approach, where most important results are established using heuristic arguments and motivated with realistic examples. With the currently available computing power, examples are not contrived to allow a closed analytical solution, and the book can concentrate on the statistical aspects of the data modelling. In addition to classical likelihood theory, the book covers many modern topics such as generalized linear models and mixed models, non parametric smoothing, robustness, the Em algorithm and empirical likelihood.

Genres: Mathematics

544 pages, Paperback

First published June 21, 2001

Reviews

[B · Rating 5 out of 5]

Some stats textbooks just list facts, but Yudi is actively thinking about statistics and how we are to understand the concept of likelihood and probability. I learned heaps from this guy.

[Zhe Sha · Rating 3 out of 5]

Should have read this book earlier!