Synopses & Reviews
It encourages students to use the statistical software package R to check examples and solve problems.
Synopsis
Providing a solid foundation of both theory and methods Statistical and Probabilistic Methods in Actuarial Science offers a unified and accessible introduction to probability and statistics for students aspiring to careers in actuarial science and insurance. This book presents practical examples of applications, specifically to general insurance, financial decision making, and risk analysis. It includes discussions of computing methods, focusing in particular on the value of simulation techniques. To facilitate student understanding, each chapter features numerous exercises. Selected solutions are contained in an appendix, and a separate solutions manual is available for lecturers.
Synopsis
Statistical and Probabilistic Methods in Actuarial Science covers many of the diverse methods in applied probability and statistics for students aspiring to careers in insurance, actuarial science, and finance. The book builds on students? existing knowledge of probability and statistics by establishing a solid and thorough understanding of these methods. It also emphasizes the wide variety of practical situations in insurance and actuarial science where these techniques may be used.
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Although some chapters are linked, several can be studied independently from the others. The first chapter introduces claims reserving via the deterministic chain ladder technique. The next few chapters survey loss distributions, risk models in a fixed period of time, and surplus processes, followed by an examination of credibility theory in which collateral and sample information are brought together to provide reasonable methods of estimation. In the subsequent chapter, experience rating via no claim discount schemes for motor insurance provides an interesting application of Markov chain methods. The final chapters discuss generalized linear models and decision and game theory.
Developed by an author with many years of teaching experience, this text presents an accessible, sound foundation in both the theory and applications of actuarial science. It encourages students to use the statistical software package R to check examples and solve problems.
Synopsis
Covering many of the diverse methods in applied probability and statistics, this book builds on readers' existing knowledge of probability and statistics by establishing a solid and thorough understanding of these methods. It also emphasizes the wide variety of practical situations in insurance and actuarial science where these techniques may be used. The applications to general insurance include loss distributions and collective risk models, reserving and experience rating, credibility estimation, and security measures of risk. The book examines generalized linear models, credibility theory, game theory, and simulation techniques and contains numerous worked examples and problems.