Publications
Chan, I. W., Badescu, A. L., & Lin, X. S. (2024). Unsupervised Detection of Anomalous Driving Patterns Using High Resolution Telematics Time Series Data. Submitted for review; patent pending. Preprint available at arXiv:2412.08106.
Chan, I. W., Tseung, S. C., Badescu, A. L., & Lin, X. S. (2024). Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behavior. North American Actuarial Journal, 1-35. Link to Article
Tseung, S. C., Chan, I. W., Fung, T. C., Badescu, A. L., & Lin, X. S. (2023). Improving risk classification and ratemaking using mixture‐of‐experts models with random effects. Journal of Risk and Insurance, 90(3), 789-820. Link to Article
Presentations
Upcoming:
Past:
Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour, 59th Actuarial Research Conference, Murfreesboro, TN, USA, July 18–20, 2024
Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour, 27th International Congress on Insurance: Mathematics and Economics, Chicago, IL, USA, July 8–11, 2024
Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour, Statistics Graduate Student Research Day, University of Toronto, Toronto, ON, Canada, April 26, 2024
The Logit-weighted Reduced Mixture of Experts (LRMoE) - A flexible modeling tool for actuarial applications, iCAS Data Science and Analytics Forum, Coronado, CA, USA, March 13, 2023
- Jupyter notebook for live demo
Mixture-of-Experts Models for Claim Frequency and Severity, CAS Ratemaking, Product and Modeling Seminar, Coronado, CA, USA, March 13-15, 2023
- Slides for presentation and Jupyter notebook for live demo