题目：Regression Tree Credibility Model

 报告人：翁成国（滑铁卢大学统计与精算系）

 时间：2017年6月19日（周一）下午13：30

 地点：博识楼434会议室

 报告摘要：

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 first 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 Buhlmann-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 flexible way, and it is particularly appealing to capture various non-linear covariates effects and/or interaction effects because no specific regression form needs to be pre-specified in the method. Our proposed RTC method automatically selects influential 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.

 报告人简介：

Chengguo Weng is an Associate Professor of Actuarial Science at the University of Waterloo. He received a Ph.D. in Actuarial Science from the University of Waterloo, a Master of Mathematics and a Bachelor of Science (both in Statistics) from Zhejiang University. His research interests span a wide range of topics in actuarial science and finance, in both theoretical and applied aspects. He has published thirty papers on internationally renowned journals in relevant areas. His latest research focuses on optimal decision, stochastic modeling and predicting problems from the fields of insurance and finance. His research team are currently working on (1) Predictive analytics in insurance and risk management; (2) Portfolio selection in high dimensional settings; (3) Actuarial risk management with basis risk; (4) Portfolio selection based on performance measures; (5) Statistical inference for general stochastic optimization problems. His homepage is at : https://www.chengguoweng.com/