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6月22日加拿大滑铁卢大学翁成国副教授讲座预告
( 来源:   发布日期:2017-06-16 阅读:次)

讲座题目Regression Tree Credibility Model

讲 座 人: Chengguo Weng Department of Statistics and Actuarial Science University of Waterloo, Canada

讲座时间:2017年622日(周四)下午15:00 -1630

讲座地点:浙江工商大学综合楼601会议室

主讲人简介:  

    翁成国,本科硕士毕业于浙江大学,博士毕业于滑铁卢大学统计与精算系,现任加拿大滑铁卢大学统计与精算系副教授,博士生导师。翁成国博士是浙江工商大学校友,曾于2004年至2005任职于统计与数学学院近两年。翁成国博士在精算学,金融数学,随机优化,统计等领域发表论文30多篇,其中多篇发表在国际顶尖精算或统计杂志,所有文章均被SCISSCI收录。获得加拿大国家自然和工程研究基金项目资助两次和北美精算学会项目两项。

摘要:

Credibility theory is one of the cornerstones in actuarial science and has been widely applied for insurance premium prediction. In this talk I will introduce our research for an SOA-funded project jointly with Dr. Liqun Diao (University of Waterloo). We bring regression trees techniques into the credibility theory and propose a novel credibility premium formula, which we call regression tree credibility (RTC) premium. The proposed RTC method rst recursively binary partitions a collective of individual risks into exclusive sub-collectives using a regression tree algorithm based on credibility loss, and then applies the classical Bu¨hlmann-Straub credibility formula to predict individual net premiums within each sub-collective. The proposed method effectively predicts individual net premiums by incorporating covariate information in a very exible way, and it is particularly appealing to capture various non-linear covariates effects and/or interaction effects because no specic regression form needs to be prespecied in the method. Our proposed RTC method automatically selects inuential covariate variables for premium prediction with no additional ex ante variable selection procedure required. The superiority in prediction accuracy of the proposed RTC model is demonstrated by extensive simulation studies.



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