Presenter: Assistant Professor Toryn Schafer, Department of Statistics at Texas A&M

Description: Model development for sequential count-valued data characterized by small counts and non-stationarities is essential for broader applicability and appropriate inference in the scientific community. Specifically, we introduce global-local shrinkage priors into a Bayesian dynamic generalized linear model to adaptively estimate both changepoints and a smooth trend for count time series. We utilize a parsimonious state-space approach to identify a dynamic signal with local parameters to track smoothness of the local mean at each time-step. This setup provides a flexible framework to detect unspecified changepoints in complex series, such as those with large interruptions in local trends. We detail the extension of our approach to time-varying parameter estimation within dynamic Negative Binomial regression analysis to identify structural breaks. Finally, we illustrate our algorithm with empirical examples in social sciences.

Bio: Toryn is an assistant professor in the Department of Statistics at Texas A&M University. Previously, she was a postdoctoral associate in the Department of Statistics and Data Science at Cornell University working with Dr. David Matteson. Her quantitative research interests span many topics, but with an underlying commonality that includes spatio-temporal modeling, Bayesian statistics, and alternative learning frameworks such as machine learning, deep learning, and reinforcement learning. She is highly motivated by scientific hypotheses with an emphasis on ecological and environmental applications.

Hosted by: Professor Paul Parker

Zoom link: https://ucsc.zoom.us/j/94523666385?pwd=T2F6eUN3dFJZRUVjaUgvdExNSVJKUT09

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