发布时间: 2018-12-03 浏览次数: 10

报告题目:Subgroup Analysis of Zero-Inflated Poisson Regression Model with Applications to Insurance Data

报告时间: 201812710:00:00

报告地点: 旭日楼306


报告内容简介:Customized personal rate offering is of growing importance in the insurance industry. To achieve this, an important step is to identify subgroups of insureds from the corresponding heterogeneous claim frequency data. In this paper, a penalized Poisson regression approach for subgroup analysis in claim frequency data is proposed. Subjects are assumed to follow a zero-inflated Poisson regression model with group-specific intercepts, which capture group characteristics of claim frequency. A penalized likelihood function is derived and optimized to identify the group-specific intercepts and effects of individual covariates. To handle the challenges arising from the optimization of the penalized likelihood function, an alternating direction method of multipliers algorithm is developed and its convergence is established. Simulations studies and real applications are provided for illustrations.

报告人简介:陈坤现为西南财经大学统计学院副教授, 研究方向为时间序列、空间统计和金融统计。陈坤曾多次赴日本早稻田大学、日本北海道大学、台湾中央研究院、香港中文大学和其他国内多所高校访问;主持参与了多项国家自然科学基金项目;《Journal of Time Series Analysis》、 《Electronic Journal of Statistics》、 《Journal of Statistical Planning and Inference》等国际重要期刊发表论文; 是《Statistica Sinica》、《Bernoulli》、《Journal of Time Series Analysis》、《Journal of Testing and Evaluation》等多个期刊的匿名审稿人。