Date: 2024-09-27

Time: 15:30-16:30 (Montreal time)

Location: Online, retransmitted in Burnside 1104

https://mcgill.zoom.us/j/88350756970

Meeting ID: 883 5075 6970

Passcode: None

Abstract:

The linear varying coefficient models posits a linear relationship between an outcome and covariates in which the covariate effects are modeled as functions of additional effect modifiers. Despite a long history of study and use in statistics and econometrics, state-of-the-art varying coefficient modeling methods cannot accommodate multivariate effect modifiers without imposing restrictive functional form assumptions or involving computationally intensive hyperparameter tuning. In response, we introduce VCBART which flexibly estimates the covariate effect in a varying coefficient model using Bayesian Additive Regression Trees. With simple default settings, VCBART outperforms existing varying coefficient methods in terms of covariate effect estimation, uncertainty quantification, and outcome prediction. Theoretically, we show that the VCBART posterior contracts at the near-minimax optimal rate. Finally, we illustrate the utility of VCBART through simulation studies and a real data application examining how the association between later-life cognition and measures of socioeconomic position vary with respect to age and sociodemographics.

Speaker

Dr. Ray Bai is an Assistant Professor of Statistics at the University of South Carolina. He joined the department in 2020 after completing a two-year postdoc at the University of Pennsylvania. He got his PhD in Statistics from the University of Florida in 2018 under the supervision of Dr. Malay Ghosh. Dr. Bai’s research spans methodology, computation, and theory, and mainly focuses on Bayesian statistics, high-dimensional statistics, and deep learning.