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Full Description
A modern practical guide to building and using actuarial models.
Loss Models: From Data to Decisions is organized around the principle that actuaries build models in order to analyze risks and make decisions about managing the risks based on conclusions drawn from the analysis. In practice, one begins with data and ends with a business decision. The book flows logically from this principle. It begins with a framework for model building and a description of frequency and severity loss data typically available to actuaries. Parametric models are emphasized throughout.
The frequency and severity models are used in building aggregate loss models, in credibility-based pricing models, and in loss analysis over multiple time periods.
Designed as both an educational text as well as a professional reference, Loss Models:
Assumes little prior knowledge of insurance systems
Features many fascinating examples taken from insurance files
Contains a major instructive case study continued through each chapter
Covers the classical areas of risk theory and loss distributions
Gives a practical but rigorous treatment of modern credibility theory
Uses standard statistical concepts, methods, and notation
Provides modern computational algorithms for implementing methods
Includes free companion software available from an FTP site
Deals with many topics on CAS 4B and SOA 151 and 152 actuarial exams
Includes many exercises based on past CAS and SOA exams.
Contents
1. Modeling 2. Random variables
3. Basic distributional quantities
4. Characteristics of actuarial models
5. Continuous models
6. Discrete distributions
7. Advanced discrete distributions
8. Frequency and severity with coverage modifications
9. Aggregate loss models
10. Introduction to mathematical statistics
11. Maximum likelihood estimation
12. Frequentist estimation for discrete distributions
13. Bayesian estimation
14. Construction of empirical models
15. Model selection
16. Introduction to limited fluctuation credibility
17. Greatest accuracy credibility
18. Empirical bayes parameter estimation
19. Simulation
