Quantile Functional Regression for Distributional Regression of Wearable Devices and Biomedical Imaging Data

Oregon Convention Center 

In this talk, we will discuss Bayesian quantile functional regression methods for regressing distributional responses on predictors with possible smooth nonlinear covariate and longitudinally varying effects. We will apply this method to biomedical imaging data from multiple sclerosis and glioblastoma patients, and activity data from wearable devices. We will discuss our general modeling framework that accommodates any number of linear or nonlinear covariates, multiple levels of random effect functions, and spatial/temporal between function correlation, plus we will describe our sparse basis function representation, Bayesian inferential approaches, and introduce functional data analysis methods to adjust for potentially nonignorable missingness in the activity data example.