Seminar by Ray Bai, University of South Carolina

VCBART: Bayesian Treed Varying Coefficient Modeling
Event Date & Time
Event Location
Bruininks Hall 220

222 Pleasant St SE
Minneapolis, MN 55455


Many studies have reported associations between later-life cognition and socioeconomic position throughout the life course. The majority of these studies, however, are unable to quantify how these associations vary over time or with respect to several demographic factors. Varying coefficient (VC) models, which treat the coefficients in a linear model as nonparametric functions of additional effect modifiers, offer an appealing way to overcome these limitations.

Unfortunately, state-of-the-art VC modeling methods require computationally prohibitive hand-tuning or make restrictive assumptions about the functional form of the coefficients. In response, we propose VCBART, which estimates each varying coefficient using Bayesian Additive Regression Trees. On both simulated and real data, with simple default hyperparameters, VCBART outperforms existing methods in terms of coefficient estimation and prediction. Theoretically, we show that the VCBART posterior concentrates around the true varying coefficients at the near-minimax rate.

Using VCBART, we predict the cognitive trajectories of 3,739 subjects from the Health and Retirement Study using multiple measures of socioeconomic position throughout the life course. We find that the association between later-life cognition and educational attainment does not vary substantially with age, but there is evidence of a persistent benefit of education after adjusting for later-life health. In contrast, the association between later-life cognition and health conditions like diabetes is negative and time-varying.


Dr. Ray Bai is an assistant professor of statistics at the University of South Carolina. He received his PhD in statistics from the University of Florida in 2018. He then completed a postdoc in biostatistics and informatics at the University of Pennsylvania Perelman School of Medicine before joining the University of South Carolina in fall 2020. His research interests include Bayesian methodology, high-dimensional statistics, spatiotemporal modeling, and analysis of electronic health records.

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