Generalized Linear Models for Insurance Data

Generalized Linear Models for Insurance Data
Author: Piet de Jong
Publisher: Cambridge University Press
Total Pages: 207
Release: 2008-02-28
Genre: Business & Economics
ISBN: 1139470477

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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.


Generalized Linear Models for Insurance Data
Language: en
Pages: 207
Authors: Piet de Jong
Categories: Business & Economics
Type: BOOK - Published: 2008-02-28 - Publisher: Cambridge University Press

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This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to suppo
Generalized Linear Models for Insurance Data
Language: en
Pages: 206
Authors:
Categories:
Type: BOOK - Published: - Publisher: Cambridge University Press

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Non-life insurance pricing is the art of setting the price of an insurance policy, taking into consideration varoius properties of the insured object and the po
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Authors: Edward W. Frees
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Type: BOOK - Published: 2016-07-27 - Publisher: Cambridge University Press

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Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrenc