Generalized Linear Models for Insurance Data (International Series on Actuarial Science)

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Product Description

This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.

Generalized Linear Models for Insurance Data (International Series on Actuarial Science) Review

I have recently completed a PhD in Actuarial Studies that involved the use of Generalized Linear Models (GLMs) to describe Life Insurance data and I have also taught GLMs to a group of Actuarial Studies students in the context of using them to describe General Insurance (aka non-life insurance or property and casualty insurance) data. From the point of view of a researcher and of an educator, I consider this book to be lacking. To me, "Generalized Linear Models for Insurance Data" feels like a set of lecture notes that would probably make sense if you attended lectures to hear the lecturer explain them, but aren't all that clear to those students who decide to skip class (given that the two authors both teach in universities, there is a good chance that this is, in fact, true).

This book can essentially be divided into two sections: the first 80 pages of the book give the background theory to generalized linear models; and the remaining 116 pages apply this theory to insurance examples. Having worked with GLMs for many years now, the first section of the book made sense to me, but I suspect that a new-comer to this material would find some parts difficult to understand. Very little detail is given on some of the more important topics; no examples are given within this initial section; and concepts that are essentially visual in nature (such as diagnostic plots) are not illustrated with graphs. The second half of the book is an improvement on the first half, with examples and illustrations making up a substantially chunk of the 116 pages. Yet, again, I feel that this section could have benefited by the concepts being discussed in greater detail. From my research and teaching, I know that, for many of these topics, de Jong and Heller have only coasted along the surface of the available information.

Exercises are given at the end of each chapter of this book, and the solutions to these can be found on the books companion website, as can the data sets used throughout this book. Some SAS code for fitting many of the models discussed in the book is given in an appendix at the back of the book, although this code is just for fitting the basic models (not for producing diagnostic plots), and is only really of use if you happen to use SAS (which I don't - I would have preferred R code, which has the advantage of being open-source, so accessible by all).

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