Modern Actuarial Risk Theory contains what every actuary needs to know about non-life insurance mathematics. It starts with the standard material like utility theory, individual and collective model and basic ruin theory. Other topics are risk measures and premium principles, bonus-malus systems, ordering of risks and credibility theory. It also contains some chapters about Generalized Linear Models, applied to rating and IBNR problems. As to the level of the mathematics, the book would fit in a bachelors or masters program in quantitative economics or mathematical statistics. This second and much expanded edition emphasizes the implementation of these techniques through the use of R. This free but incredibly powerful software is rapidly developing into the de facto standard for statistical computation, not just in academic circles but also in practice. With R, one can do simulations, find maximum likelihood estimators, compute distributions by inverting transforms, and much more.
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Editorial Reviews
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From the reviews of the second edition: "The book gives a comprehensive survey of non-life insurance mathematics. … Originally written for use with the actuarial science programs at the Universities of Amsterdam and Leuven, it is now in use at many other universities as well as for the non-academic actuarial education program organized by the Dutch Actuarial Society. The methods presented can not only be used in non-life insurance, but also in other branches of actuarial science, as well as in actuarial practice." (Pavel Stoynov, Zentralblatt MATH, Vol. 1148, 2008)
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Modern Actuarial Risk Theory contains what every actuary needs to know about non-life insurance mathematics. It starts with the standard material like utility theory, individual and collective model and basic ruin theory. Other topics are risk measures and premium principles, bonus-malus systems, ordering of risks and credibility theory. It also contains some chapters about Generalized Linear Models, applied to rating and IBNR problems. As to the level of the mathematics, the book would fit in a bachelors or masters program in quantitative economics or mathematical statistics. This second and much expanded edition emphasizes the implementation of these techniques through the use of R. This free but incredibly powerful software is rapidly developing into the de facto standard for statistical computation, not just in academic circles but also in practice. With R, one can do simulations, find maximum likelihood estimators, compute distributions by inverting transforms, and much more.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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translated gamma approximation, arithmetic separation method, open collective model, chain ladder method, sparse vector algorithm, comonotonic upper bound, various premium principles, exponential premium, chain ladder model, claim severity distribution, exponential dispersion family, collective risk models, claim number distribution, unbiasedness restriction, normal power approximation, zero utility premium, comonotonic random variables, individual risk model, mean value principle, ruin probability, traditional estimation procedures, credibility premium, claim size distribution, related joint distributions, exponential claims Key Phrases - Capitalized Phrases (CAPs): (learn more)
Exercises Section, Fast Fourier Transform, Central Limit Theorem, Normal Power, Tweedie's Compound Poisson-gamma Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
translated gamma approximation, arithmetic separation method, open collective model, chain ladder method, sparse vector algorithm, comonotonic upper bound, various premium principles, exponential premium, chain ladder model, claim severity distribution, exponential dispersion family, collective risk models, claim number distribution, unbiasedness restriction, normal power approximation, zero utility premium, comonotonic random variables, individual risk model, mean value principle, ruin probability, traditional estimation procedures, credibility premium, claim size distribution, related joint distributions, exponential claims Key Phrases - Capitalized Phrases (CAPs): (learn more)
Exercises Section, Fast Fourier Transform, Central Limit Theorem, Normal Power, Tweedie's Compound Poisson-gamma Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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