Overview

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 of 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 simple comparison of two accident rates to complex studies that require generalized linear or semiparametric modelling.

Key Emphasis

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 data modelling.

Topics Covered

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.