Buch, Englisch, 320 Seiten, Format (B × H): 156 mm x 235 mm
Buch, Englisch, 320 Seiten, Format (B × H): 156 mm x 235 mm
Reihe: Chapman and Hall/CRC Financial Mathematics Series
ISBN: 978-1-4665-1061-6
Verlag: Taylor & Francis
There has been a rapidly growing interest in Bayesian methods among insurance practitioners in recent years, mainly because of their ability to generate predictive distributions and to rigorously incorporate expert opinion through prior probabilities. This book introduces modern Bayesian modeling techniques for actuarial and insurance applications. It first provides the necessary background in current actuarial practice and then presents Bayesian methods and MCMC. It includes advanced techniques, such as nonlinear modeling, as well as three chapters on model selection and averaging. The text features case studies using real actuarial and insurance data with computations in R and WinBUGS.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
INTRODUCTION TO CURRENT PRACTICE
Actuarial Background and Introduction to Insurance
Traditional Models for Insurance Reserving and Pricing
Case studies: Generalized Linear Models in Insurance
Hierarchical Linear Models with Insurance Applications
BAYESIAN LINEAR AND GENERALIZED LINEAR MODELS
Introduction to Bayesian Statistics
Inference in Bayesian Linear Models
Introduction to Bayesian Computing: Markov Chain Monte Carlo
Case Studies: Bayesian Generalized Linear Models in Insurance
ADVANCED BAYESIAN METHODS AND CASE STUDIES
Bayesian Semi-parametric Models
Bayesian Non-Linear Models
Bayesian Copula Models
MODEL SELECTION AND AVERAGING
Model Selection Strategies
Bayesian Model Averaging
Case Study: When to Select and When to Average Your Models
Appendix
Appendix W: Introduction to WinBUGS
Appendix R: Introduction to R
Appendix C: Code