北航经管学院“经济统计论坛”系列讲座
（2024年第1期，总第23期）
墨尔本大学Han Li副教授讲座通知
讲座题目：Boosting domain-specific models with shrinkage: An application in mortality forecasting
讲座时间：2024年5月27日（周一），10:00-11:30
会议地址：新主楼 A949
讲座嘉宾：Han Li 副教授
讲座嘉宾
Dr Han Li is an Associate Professor at the Department of Economics, the University of Melbourne. She received a Bachelor of Commerce (Honours) degree in Actuarial Studies at the University of Melbourne and completed her PhD degree in Econometrics and Business Statistics at Monash University. During 2016-2021, she has held academic positions at the University of New South Wales and Macquarie University.
She has a broad range of research interests around longevity and mortality risks, ageing and retirement, and the impact of climate change on insurance industry. Specifically, much of her research expertise centers on actuarial modelling and forecasting using advanced econometric and statistical techniques. She has attracted research funds from the Australian Research Council, the Society of Actuaries, the Casualty Actuarial Society, and the Australia-Germany Joint Research Cooperation Scheme (DAAD). Han's research has been published in top-tier journals including Insurance: Mathematics and Economics, ASTIN Bulletin, North American Actuarial Journal, Scandinavian Actuarial Journal, Journal of Forecasting, International Journal of Forecasting, and Annals of Actuarial Science.
邀请人：康雁飞 副教授
讲座概要
This paper extends the technique of gradient boosting with a focus on using domain-specific models instead of trees. The domain of mortality forecasting is considered as an application. The two novel contributions are to use well-known stochastic mortality models as weak learners in gradient boosting rather than trees, and to include a penalty that shrinks the forecasts of mortality in adjacent age groups and nearby geographical regions closer together. The proposed method demonstrates superior forecasting performance based on US male mortality data from 1969 to 2019. The proposed approach also enables us to interpret and visualize the results. The boosted model with age-based shrinkage yields the most accurate national-level mortality forecast. For state-level forecasts, spatial shrinkage provides further improvement in accuracy in addition to the benefits achieved by age-based shrinkage. This additional improvement can be attributed to data sharing across states with both large and small populations in adjacent regions, as well as states which share common risk factors.
