Event Date
Speaker: Ray Bai (Assistant Professor, Department of Statistics, University of South Carolina)
Title: "Efficient Algorithms and Theory for High-Dimensional Bayesian Varying Coefficient Models"
Abstract: Varying coefficient models are useful for modeling time-varying effects on responses that are measured repeatedly. In this talk, we introduce the nonparametric varying coefficient spike-and-slab lasso (NVC-SSL) for Bayesian estimation and variable selection in NVC models. The NVC-SSL simultaneously selects and estimates the significant varying coefficients, while also accounting for temporal correlations. Our model can be implemented using a computationally efficient expectation-maximization (EM) algorithm, thus avoiding the computational intensiveness of Markov chain Monte Carlo (MCMC) in high dimensions. We also employ a simple method to make our model robust to misspecification of the temporal correlation structure. In contrast to frequentist approaches, little is known about the large-sample properties for Bayesian varying coefficients in high dimensions. In this talk, we take a step towards narrowing this gap by studying posterior contraction rates for the NVC-SSL model when p>n under both correct specification and misspecification of the temporal correlation structure. We illustrate our methodology through simulation studies and data analysis.
About the speaker: Ray Bai is an Assistant Professor in the Department of Statistics at the University of South Carolina. His research area focuses on high-dimensional modeling and scalable machine learning algorithms for large and complex data sets.
Seminar Date/Time: Thursday April 29, 4:10pm (U.S. Pacific Standard Time)
This seminar will be delivered remotely via Zoom. To access the Zoom meeting for this seminar, please contact the instructor Professor Jairo Fùquene Patiño or Pete Scully (pscully@ucdavis.edu) for the meeting ID and password, stating your affiliation.