Adaptive Design Theory and Implementation Using SAS and R, Second Edition

Thomas M. Braun
2015 International Statistical Review  
Readership: Students, researchers and practitioners with a background in actuarial science. The main motivation of the book (and of computational actuarial science in general) is related to 'bring new life into the teaching of actuarial science in colleges and universities' and to 'provide an opportunity for students to move away from too much use of lecture-exam paradigm and more use of a laboratory paper paradigm in teaching' (statement rephrased from Kendrick et al. (2006)). In fact, the
more » ... objective of the book is that the reader gets interested in the topic and plays with the presented models and R codes in an active way. I have experienced that this goal can be easily reached for a large audience of readers, because the presentation of the various arguments encourages an active learning of the concepts 'without being burdened by the theory'. The book starts with a preliminary chapter that introduces basic aspects of the R language, mentioning also more advanced methods such as parallel computing and C/CCC embedded codes. Then it is divided into four parts dealing with methodology (including Bayesian methods), life insurance, finance and non-life insurance. All the chapters are independent (at least in the computational part). Most of them present a list of exercises, which are very useful for didactic purposes. Although the emphasis is on the computational aspects, a long list of references to theoretical work is also provided at the end of the book. In my opinion, this is a reference book for people interested in computational aspects of actuarial science. In order to understand its real value, it is necessary that the readers also 'get their hands dirty' and try the different codes and datasets that have been provided. To this end, the R codes presented in this book can be found on github, although some codes are still missing. Moreover, the package that contains all the datasets can be found on
doi:10.1111/insr.12143 fatcat:prwxfuohmbf4hnk5chku4lqphe