Generalized Linear Models for Insurance Data 1st edition by Piet de Jong, Gillian Heller – Ebook PDF Instant Download/Delivery: 0521879140 , 978-0521879149
Full download Generalized Linear Models for Insurance Data 1st edition after payment
Product details:
ISBN 10: 0521879140
ISBN 13: 978-0521879149
Author: Piet de Jong, Gillian Heller
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 1st Table of contents:
1. Insurance data
1.1. Introduction
1.2. Types of variables
1.3. Data transformations
1.4. Data exploration
1.5. Grouping and runoff triangles
1.6. Assessing distributions
1.7. Data issues and biases
1.8. Data sets used
1.9. Outline of rest of book
2. Response distributions
2.1. Discrete and continuous random variables
2.2. Bernoulli
2.3. Binomial
2.4. Poisson
2.5. Negative binomial
2.6. Normal
2.7. Chi-square and gamma
2.8. Inverse Gaussian
2.9. Overdispersion
Exercises
3. Exponential family responses and estimation
3.1. Exponential family
3.2. The variance function
3.3. Proof of the mean and variance expressions
3.4. Standard distributions in the exponential family form
3.5. Fitting probability functions to data
Exercises
4. Linear modeling
4.1. History and terminology of linear modeling
4.2. What does “linear” in linear model mean?
4.3. Simple linear modeling
4.4. Multiple linear modeling
4.5. The classical linear model
4.6. Least squares properties under the classical linear model
4.7. Weighted least squares
4.8. Grouped and ungrouped data
4.9. Transformations to normality and linearity
4.10. Categorical explanatory variables
4.11. Polynomial regression
4.12. Banding continuous explanatory variables
4.13. Interaction
4.14. Collinearity
4.15. Hypothesis testing
4.16. Checks using the residuals
4.17. Checking explanatory variable specifications
4.18. Outliers
4.19. Model selection
5. Generalized linear models
5.1. The generalized linear model
5.2. Steps in generalized linear modeling
5.3. Links and canonical links
5.4. Offsets
5.5. Maximum likelihood estimation
5.6. Confidence intervals and prediction
5.7. Assessing fits and the deviance
5.8. Testing the significance of explanatory variables
5.9. Residuals
5.10. Further diagnostic tools
5.11. Model selection
Exercises
6. Models for count data
6.1. Poisson regression
6.2. Poisson overdispersion and negative binomial regression
6.3. Quasi-likelihood
6.4. Counts and frequencies
Exercises
7. Categorical responses
7.1. Binary responses
7.2. Logistic regression
7.3. Application of logistic regression to vehicle insurance
7.4. Correcting for exposure
7.5. Grouped binary data
7.6. Goodness of fit for logistic regression
7.7. Categorical responses with more than two categories
7.8. Ordinal responses
7.9. Nominal responses
Exercises
8. Continuous responses
8.1. Gamma regression
8.2. Inverse Gaussian regression
8.3. Tweedie regression
Exercises
9. Correlated data
9.1. Random effects
9.2. Specification of within-cluster correlation
9.3. Generalized estimating equations
Exercise
10. Extensions to the generalized linear model
10.1. Generalized additive models
10.2. Double generalized linear models
10.3. Generalized additive models for location, scale and shape
10.4. Zero-adjusted inverse Gaussian regression
10.5. A mean and dispersion model for total claim size
People also search for Generalized Linear Models for Insurance Data 1st :
generalized linear models for insurance data r code
generalized linear models for insurance data pdf
generalized linear models for insurance data piet de jong pdf
generalized linear models for insurance data cambridge university press
what is a generalized linear model
Tags: Piet de Jong, Gillian Heller, Generalized Linear, Insurance Data