2022-10-03 Stat Cafe - Dr. Schafer

• Time: Monday 10/03 from 11:30 AM to 12:30 PM
• Location: BLOC 503
• Snacks and drinks will be provided
• Gallery

Topic

Trend Filtering with Adaptive Bayesian Changepoint Analysis for Count Time Series

Abstract

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.