Comet Calendar
Statistics Seminar by Liang Hong (UT Dallas)
Friday, November 4, 2022 11am to 12pm
Virtual Event
Event Details
Prediction of insurance claims based on a machine learning strategy
Bias resulting from model misspecification is a concern in insurance predictive modeling. Indeed, this bias puts an insurance data scientist at risk of making invalid or unreliable predictions. A method that could provide provably valid predictions uniformly across a large class of possible claims distributions would effectively eliminate the risk of model misspecification bias. Conformal prediction, a machine learning strategy, is one such method that can meet this need. In this work, we tailor this approach to the typical insurance application and show that the predictions are not only valid but also efficient across a wide range of settings.
Location
Virtual Event
Department
- Natural Sciences & Mathematics
Contact Information
- Qiwei Li
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UTD strives to create inclusive and accessible events in accordance with the Americans with Disabilities Act (ADA). If you require an accommodation to fully participate in this event, please contact the event coordinator (listed above) at least 10 business days prior to the event. If you have any additional questions, please email ADACoordinator@utdallas.edu and the AccessAbility Resource Center at accessability@utdallas.edu.
