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

主办单位：英国立博官网中文版

报告专家：陈坤

专家所在单位：西南财经大学

报告时间：2020年12月24日13:00-14:00

报告地点：腾讯会议（会议ID：793 481 969）

专家简介：陈坤，西南财经大学统计学院副教授、博士生导师，研究方向为时间序列分析、空间统计、函数型数据分析和金融统计。曾多次赴日本早稻田大学、日本北海道大学、台湾中研院、香港中文大学、浙江大学和其他国内外多所高校访问。主持并参与了多项国家自然科学基金项目、教育部人文社科项目；在《Insurance: Mathematics and Economics》、《Journal of Time Series Analysis》、《Electronic Journal of Statistics》、 《Canadian 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 Forecasting》等多个期刊的匿名审稿人。

报告摘要：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.

邀请人：刘展