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Titlebook: Lundberg Approximations for Compound Distributions with Insurance Applications; Gordon E. Willmot,X. Sheldon Lin Book 2001 Springer Scienc

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书目名称Lundberg Approximations for Compound Distributions with Insurance Applications
编辑Gordon E. Willmot,X. Sheldon Lin
视频video
丛书名称Lecture Notes in Statistics
图书封面Titlebook: Lundberg Approximations for Compound Distributions with Insurance Applications;  Gordon E. Willmot,X. Sheldon Lin Book 2001 Springer Scienc
描述These notes represent our summary of much of the recent research that has been done in recent years on approximations and bounds that have been developed for compound distributions and related quantities which are of interest in insurance and other areas of application in applied probability. The basic technique employed in the derivation of many bounds is induc­ tive, an approach that is motivated by arguments used by Sparre-Andersen (1957) in connection with a renewal risk model in insurance. This technique is both simple and powerful, and yields quite general results. The bounds themselves are motivated by the classical Lundberg exponential bounds which apply to ruin probabilities, and the connection to compound dis­ tributions is through the interpretation of the ruin probability as the tail probability of a compound geometric distribution. The initial exponential bounds were given in Willmot and Lin (1994), followed by the nonexpo­ nential generalization in Willmot (1994). Other related work on approximations for compound distributions and applications to various problems in insurance in particular and applied probability in general is also discussed in subsequent chapters. Th
出版日期Book 2001
关键词Binomial distribution; G/G/1 queue; Martingale; Poisson distribution; binomial; modeling; queueing theory;
版次1
doihttps://doi.org/10.1007/978-1-4613-0111-0
isbn_softcover978-0-387-95135-5
isbn_ebook978-1-4613-0111-0Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer Science+Business Media New York 2001
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Introduction,he literature on such models is voluminous, both from an analytic and a numerical viewpoint. The difficulty in evaluation arises from the presence of convolutions, but such evaluation is important for many applications.
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Book 2001ped for compound distributions and related quantities which are of interest in insurance and other areas of application in applied probability. The basic technique employed in the derivation of many bounds is induc­ tive, an approach that is motivated by arguments used by Sparre-Andersen (1957) in c
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