報告題目：Subgroup Analysis of Zero-Inflated Poisson Regression Model with Applications to Insurance Data

報告時間： 2018年12月7日 10:00:00

報告地點： 旭日樓306

報告人：陳坤

報告内容簡介：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.

報告人簡介：陳坤現為西南财經大學統計學院副教授, 研究方向為時間序列、空間統計和金融統計。陳坤曾多次赴日本早稻田大學、日本北海道大學、台灣中央研究院、香港中文大學和其他國内多所高校訪問；主持參與了多項國家自然科學基金項目；《Journal of Time Series Analysis》、 《Electronic Journal of Statistics》、 《Journal of Statistical Planning and Inference》等國際重要期刊發表論文； 是《Statistica Sinica》、《Bernoulli》、《Journal of Time Series Analysis》、《Journal of Testing and Evaluation》等多個期刊的匿名審稿人。