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Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques (International Series on Actuarial Science)
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Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practicing analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.
- Sales Rank: #873572 in Books
- Published on: 2014-07-28
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x 1.46" w x 6.85" l, 2.45 pounds
- Binding: Hardcover
- 563 pages
Review
"With contributions coming from a wide variety of researchers, professors, and actuaries - including several CAS Fellows - it's clear that this book will be valuable for any P and C actuary whose main concern is using predictive modeling in his or her own work."
David Zornek, Actuarial Review
About the Author
E. W. Frees is a Professor of Business at the University of Wisconsin-Madison and is holder of the Fortis Health Insurance Professorship of Actuarial Science. He is a Fellow of both the Society of Actuaries and the American Statistical Association. He has served in several editorial capacities including Editor of the North American Actuarial Journal and Associate Editor for Insurance: Mathematics and Economics. An award-winning researcher, he as published in the leading refereed academic journals in Business and Economics and Theoretical and Applied Statistics.
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Most helpful customer reviews
7 of 8 people found the following review helpful.
A Welcome New Resource on an Important Subject
By H I Weisberg
Predictive modeling has become a hot topic in light of the new era of Big Data that is emerging. It occupies a unique position at the intersection of traditional statistical methods and machine learning algorithms. Practitioners of the art of model-building can benefit from the knowledge base provided by both disciplines. This excellent compendium focuses mainly on the statistical side of the equation. It offers comprehensive coverage of the relevant statistical techniques and can serve as a valuable reference and resource. The editors should be commended for the high quality of both the content and writing maintained throughout.
Each chapter is almost a stand-alone mini-textbook on a particular method, and is introduced by a brief and informative summary. Each method is illustrated with well-developed insurance-related applications and R code for implementing the analyses. The presentation is sometimes at a fairly high mathematical level, but remains firmly moored to practical applications. Although the context is actuarial science, it may well be of interest to a wider audience.
Perhaps the editors can be persuaded to undertake an equally impressive volume devoted to the machine-learning side of predictive modeling. Personally, I would like to see chapters dealing with various popular machine-learning techniques such as CART, Lasso, neural nets, genetic algorithms, KNN etc. Chapters on such supervised learning techniques would complement the excellent chapter by Louise Francis on unsupervised learning in the current volume. In any case, I look forward to the second planned companion volume that will present a wide range of case studies on actual projects.
4 of 4 people found the following review helpful.
Five Stars
By AR
Data and associated R code is available at: http://instruction.bus.wisc.edu/jfrees/jfreesbooks/PredictiveModelingVol1/index.htm
1 of 1 people found the following review helpful.
A useful practitioner's reference not limited to insurance
By Daniel C
This book has a wealth of information and, in general, is written with enough of both mathematical rigor and practical worked examples. Being a collection of almost independently written chapters, its exposition style not always internally consistent, however, if you are willing to overlook that and are willing to put some effort into understanding what is being said, you will find it quite useful. Refreshingly, it covers both traditional and Bayesian approach to regression modeling. Even though it is advertised as a reference for insurance professionals, the material covered applies to virtually any area of predictive analytics, including healthcare, market research, reliability and fraud analysis. Given its value to the practitioner, it is definitely a worthy purchase. I am looking forward to Volume II.
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