报 告 人：Lei Hua 博士
报告题目：Tail negative dependence and its applications for aggregate loss modeling
报告时间：2015年5月28日（周四）下午3:00
报告地点：静远楼1508学术报告厅
Abstract: Tail order of copulas can be used to describe the strength of dependence in the tails of a joint distribution. When the value of tail order is larger than the dimension, it may lead to tail negative dependence. First of all, we prove results on conditions that lead to tail negative dependence for Archimedean copulas. Then we construct new copulas that possess upper tail negative dependence. In particular, a copula based on a scale mixture with a generalized gamma random variable (GGS copula) is useful for modeling asymmetric tail negative dependence structures. Finally, we apply mixed copula regression basedon the GGS copula to aggregate loss modeling for a medical expenditure panelsurvey dataset. We find that there exists upper tail negative dependence between loss frequency and loss severity for this dataset, and the introduction of tail negative dependence structures significantly improves the aggregateloss modeling.
Lei Hua 博士个人简介：
Lei Hua got his PhD degree from the University of British Columbia in 2012. He is an Assistant Professor in the Division of Statistics at Northern Illinois University. His research interest includes multivariate dependence modeling, extreme value theory, quantitative risk management and actuarial theory and applications. His research has been supported by Society of Actuaries and Casualty Actuarial Society.