报告人：Tsz Chai Fung
报告主题：Soft splicing model: Bridging the gap between composite model and finite mixture model
报告摘要：Considerations of both the heavy-tail phenomenon and multi-modality of a claim severity distribution have been challenging in the actuarial literature and practices. In this article, we develop a novel class of soft splicing models that bridges the gap between pre-existing methods for handling the issues above. The proposed method is flexible enough to incorporate tail-heaviness and multi-modality with computational efficiency and nests finite mixture models and splicing models as its special and/or limiting cases. The soft splicing model is also more robust in extrapolating the tail-heaviness of distribution subject to model contamination. According to simulation studies and real insurance claim data analyses, it is shown that the proposed soft splicing model provides superior goodness-of-fit and more accurate estimates of tail risk measures than both finite mixture and composite models.
报告人简介：Tsz Chai (Samson) Fung, FSA, is an Assistant Professor in the Maurice R. Greenberg School of Risk Science, Georgia State University (GSU). Prior to joining GSU, he worked as a Postdoctoral Researcher at ETH Zurich under the supervision of Professor Mario Wuthrich during 2020-2021. He earned a PhD Statistics degree at the University of Toronto in 2020, supervised by Professor Sheldon Lin and Andrei Badescu, and he was also awarded the Hickman Scholarship and the Ontario trillium scholarship during his PhD study. His current research interests include insurance loss modeling, flexible statistical models, and inference techniques with applications in actuarial problems, including pricing, reserving, and risk management. He has actively published in leading actuarial and statistics journals, including IME, ASTIN Bulletin, NAAJ, SAJ, JRI and JRSS.