Longitudinal First hitting-time Regression Models with Extension to Neural Network
- 2026-01-05 (Mon.), 11:00 AM
- Auditorium, B1F, Institute of Statistical Science；The tea reception will be held at 09:40.
- Online live streaming through Microsoft Teams will be available.
- Prof. Mei-Ling Ting Lee
- Department of Epidemiology & Biostatistics, University of Maryland, College Park
Abstract
Disease progression in a patient can be described mathematically as a stochastic process. The patient experiences a failure event when his/her disease progression first reaches a critical threshold level. This happening defines the failure event itself and the first hitting time (FHT) is the event time. First hitting- time based threshold regression models incorporate regression functions for parameters of the underlying stochastic process. The FHT model is extended to the Levy family with stationary independent increments and a cumulant generating function. Extension to neural network applications will also be discussed.
Keywords: Levy processes, non-proportional hazards, Wiener processes.
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