A0426
Title: Multiple change point clustering of count processes
Authors: Shuchismita Sarkar - Bowling Green State University (United States) [presenting]
Abstract: A model-based clustering algorithm relying on a finite mixture of negative binomial Levy processes is proposed. The algorithm models heterogeneous stochastic count process data and automatically estimates multiple change points upon fitting the mixture model. Such a change point estimation identifies time points when deviation from the standard process has occurred and serves as an important diagnostic tool for analyzing temporal data. The proposed model is applied to the COVID-positive ICU cases in the state of California with very interesting results.
