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http://dx.doi.org/10.7465/jkdi.2013.24.4.689

A study of Bayesian inference on auto insurance credibility application  

Kim, Myung Joon (Department of Business Statistics, Hannam University)
Kim, Yeong-Hwa (Department of Applied Statistics, Chung-Ang University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.4, 2013 , pp. 689-699 More about this Journal
Abstract
This paper studies the partial credibility application method by assuming the empirical prior or noninformative prior informations in auto insurnace business where intensive rating segmentation is expanded because of premium competition. Expanding of rating factor segmetation brings the increase of pricing cells, as a result, the number of cells for partial credibility application will increase correspondingly. This study is trying to suggest more accurate estimation method by considering the Bayesian framework. By using empirically well-known or noninformative information, inducing the proper posterior distribution and applying the Bayes estimate which is minimizing the error loss into the credibility method, we will show the advantage of Bayesian inference by comparison with current approaches. The comparison is implemented with square root rule which is a widely accepted method in insurance business. The convergence level towarding to the true risk will be compared among various approaches. This study introduces the alternative way of redcuing the error to the auto insurance business fields in need of various methods because of more segmentations.
Keywords
Bayesian inference; noninformative prior; partial credibility; risk segmentation;
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Times Cited By KSCI : 1  (Citation Analysis)
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