Titlebook: Bayesian Statistics in Actuarial Science; with Emphasis on Cre Stuart A. Klugman Book 1992 Springer Science+Business Media New York 1992 ac

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Prediction with Parameter Uncertainty,l form, but unknown parameters. Of interest is the value of a future observation whose distribution also depends on these parameters. Of course, this is the traditional actuarial problem. The observations are the benefits paid in the past to policyholders and we desire to predict the payments that w

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The Credibility Problem,quantity whose ultimate value will be affected by a number of factors: the individual characteristics of the insured, the characteristics of a larger group to which the insured belongs, external factors (mostly economic quantities), and the random nature of the insured event. Recognizing that no amo

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Modifications to the Hierarchical Normal Linear Model,s—lognormal and Poisson. In all cases the normal distribution is retained for the second level. This can usually be accomplished by careful parametrization of the first level parameters. The third modification presented is a general method for dealing with non-normal distributions.

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0924-5014 icable to any structured estimation problem. In particular, statisticians will recognize that the basic credibility problem has the same setting as the random e978-90-481-5790-7978-94-017-0845-6Series ISSN 0924-5014

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Book 1992 to provide evidence concerning the quality of any estimates. While the examples are all actuarial in nature, the methods discussed are applicable to any structured estimation problem. In particular, statisticians will recognize that the basic credibility problem has the same setting as the random e

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https://doi.org/10.1007/978-3-030-85540-6nsisting of confidence intervals and hypothesis tests is certainly the most widely used and takes up the vast majority, if not all, of the typical statistics text. On the other hand, “Bayesian” statistics, a mode of inference based on Bayes’ Theorem, has attracted a small group of passionate support