Customer Reviews

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

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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).

This is a great book on usage of glm in insurance business. It is not the book for learning theory of glm as the other review is correctly pointing out. It gives hands on experience and examples are great.