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Financial Math Seminar

Gradient tree-boosted mixture models and their applications in insurance loss prediction

  • 演讲者:高光远(中国人民大学)

  • 时间:2021-11-18 16:00-17:00

  • 地点:腾讯会议 ID 149 277 550

Abstract

Insurance loss data often cannot be well modeled by a single distribution. Mixture of models are often applied in insurance loss modeling. The Expectation-Maximization (EM) algorithm is used for parameter estimation in mixture of models. Feature engineering and variable selection are challenging for mixture of models due to several component models involving. Overfitting is also a concern when predicting future loss. To address those issues, we propose an Expectation-Boosting (EB) algorithm, which replaces the maximization step in the EM algorithm by a gradient boosting decision tree. The boosting is overfitting-sensitive, and it performs automated feature engineering, model fitting and variable selection simultaneously. The EB algorithm fully explores the predictive power of covariate space. We illustrate those advantages using two simulated data and a real insurance loss data.


个人简介:高光远,中国人民大学统计学院副教授。主要研究领域包括非寿险准备金评估方法、贝叶斯统计、车险定价模型、车联网大数据分析、copulas、死亡率预测模型等。研究成果发表在《ASTIN Bulletin》、《Insurance:Mathematics and Economics》、《Machine Learning》等,由Springer出版著作《Bayesian claims reserving methods in non-life insurance with Stan》。参与编著多本教材,建设慕课《金融数学》、《非寿险精算学》。主持国家自科青年项目、Society of Actuaries科研项目等,参与国家社科重大项目等。