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.
This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications.
The book focuses on methods based on GLMs that have been found useful in actuarial practice and provides a set of tools for a tariff analysis.
The book focuses on methods based on GLMs that have been found useful in actuarial practice and provides a set of tools for a tariff analysis.
Generalized Linear Models for Insurance Rating
Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes.
This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data.
As to the level of the mathematics, the book would fit in a bachelors or masters program in quantitative economics or mathematical statistics. This second and.
The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and applications.
This book is about making machine learning models and their decisions interpretable.