Measurement Error in Nonlinear Models: A Modern Perspective

This is the second edition of a classical text, which provides a comprehensive update of the original material, that was first published in 1995. The book tackles the difficult problem of variables in a prediction model that are themselves subject to error—a rather common problem, which is often overlooked or ignored in practical situations. It presents a variety of approaches to addressing these problems, from both the functional and the structural modelling viewpoints, and provides a wide range of illustrative examples demonstrating the methods for a variety of non-linear models. Since the first edition, the field of measurement error modelling has been a keen research area, and this new edition both updates and extends previous chapters as well as including some new topics. The chapter on Bayesian methods has been greatly extended, as has some of the more technical material such as issues surrounding score function methods. There are new chapters on measurement errors in mixed and longitudinal models, survival models and semiparametric regression models. One of the highlights, which has been preserved from the first edition, is the way that chapters are split into descriptions of methods to handle measurement errors, and applications of these methods to specific models. This results in a book which is a valuable resource when attempting to understand the theoretical aspects of measurement error models, while also being useful in practical situations. From the practitioners’ viewpoint, although it is said that standard statistical software can be used to fit the models that are described in the book, this is not covered in detail, leaving this very much as a task for the reader. Overall, this is clearly a book of great value to those with an interest in measurement error modelling in non-linear situations as well as being a useful resource for practical applied statisticians. I have no doubt that the second edition will prove to be as valuable a resource as the first has been.