Generalized Linear Models for Insurance Data [Hardcover]

All techniques illustrated on data sets relevant to insurance; SAS code and output, data sets, exercise solutions on websiteActuaries should have the tools they need. Practical and rigorous, this books introduces GLMs in the actuarial context. All techniques are illustrated on data sets relevant to insurance. Exercises and data-based practicals let readers consolidate skills. SAS code and output, data sets, exercise solutions on website.Actuaries should have the tools they need. Practical and rigorous, this books introduces GLMs in the actuarial context. All techniques are illustrated on data sets relevant to insurance. Exercises and data-based practicals let readers consolidate skills. SAS code and output, data sets, exercise solutions on website.This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.Preface; 1. Insurance data; 2. Response distributions; 3. Exponential family responses and estimation; 4. Linear modeling; 5. Generalized linear models; 6. Models for count data; 7. Categorical responses; 8. Continuoul£P