Review
"...gives the reader a good insight." (Allianz Global Risk Report, Vol 13, 2000)
"...gives the reader a good insight." (Allianz Global Risk Report, Vol 13, 2000)
Book 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.
Book Info
Organized around the principle that actuaries build models in order to analyze risks & make decisions about managing the risks based on conclusions drawn from the analysis. DLC: Insurance - Statistical methods.
The publisher, John Wiley & Sons
Much of actuarial science consists of constructing and analyzing mathematical models that describe how fluids flow into and out of an insurance system. This book examines contemporary topics such as risk theory and economics, credibility and stochastic processes with a focus on the loss process, or the outflow of cash due to the payment of benefits.
From the Back Cover
"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.
About the Author
STUART A. KLUGMAN, PhD, is a professor in the College of Business and Public Administration of Drake University, Des Moines, Iowa.
HARRY H. PANJER, PhD, teaches in the Department of Statistics and Actuarial Science of the University of Waterloo, Ontario.
GORDON E. WILLMOT, PhD, teaches in the Department of Statistics and Actuarial Science of the University of Waterloo.
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