Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques

This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.

Edward W. Frees (Edited by), Richard A. Derrig (Edited by), Glenn Meyers (Edited by)

9781107029873, Cambridge University Press

Hardback, published 28 July 2014

563 pages, 120 b/w illus. 94 tables 26 exercises
25.5 x 17.9 x 3.7 cm, 1.12 kg

'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

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 practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong 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.

1. Predictive modeling in actuarial science Edward W. Frees and Richard A. Derrig
Part I. Predictive Modeling Foundations: 2. Overview of linear models Marjorie Rosenberg
3. Regression with categorical dependent variables Montserrat Guillen
4. Regression with count-dependent variables Jean-Philippe Boucher
5. Generalized linear models Curtis Gary Dean
6. Frequency and severity models Edward W. Frees
Part II. Predictive Modeling Methods: 7. Longitudinal and panel data models Edward W. Frees
8. Linear mixed models Katrien Antonio and Yanwei Zhang
9. Credibility and regression modeling Vytaras Brazauskas, Harald Dornheim and Ponmalar Ratnam
10. Fat-tailed regression models Peng Shi
11. Spatial modeling Eike Brechmann and Claudia Czado
12. Unsupervised learning Louise Francis
Part III. Bayesian and Mixed Modeling: 13. Bayesian computational methods Brian Hartman
14. Bayesian regression models Luis Nieto-Barajas and Enrique de Alba
15. Generalized additive models and nonparametric regression Patrick L. Brockett, Shuo-Li Chuang and Utai Pitaktong
16. Non-linear mixed models Katrien Antonio and Yanwei Zhang
Part IV. Longitudinal Modeling: 17. Time series analysis Piet de Jong
18. Claims triangles/loss reserves Greg Taylor
19. Survival models Jim Robinson
20. Transition modeling Bruce Jones and Weijia Wu.

Subject Areas: Probability & statistics [PBT], Insurance & actuarial studies [KFFN], Finance [KFF]