Title: Bayesian regression models for big spatially or longitudinally correlated functional data
Authors: Jeff Morris - MD Anderson Cancer Center (United States) [presenting]
Lin Zhang - University of Minnesota (United States)
Hojin Yang - The University of Texas MD Anderson Cancer Center (United States)
Wonyul Lee - Food and Drug Administration (United States)
Hongxiao Zhu - Virginia Tech (United States)
Veerabhadran Baladandayuthapani - UT MD Anderson Cancer Center (United States)
Abstract: A series of regression modeling strategies that can be used for high-dimensional spatially- or longitudinally correlated functional data will be described. Intrafunctional correlation is handled through basis function modeling, while interfunctional correlation is captured by one of three approaches: (1) parametric or nonparametric random effect functions, (2) separable or non-separable spatial (or temporal) inter-functional processes, or (3) tensor-basis function modeling. Rigorous Bayesian inference is done in such a way that adjusts for any potential multiple testing issues. We will describe these general approaches and illustrate them on a series of complex, high-dimensional, spatially and longitudinally correlated functional data sets coming from strain tensor data from a glaucoma study, bladder cancer genomic maps and event-related potential data from a smoking cessation study. We will also discuss recent work in which we have developed spatiotemporal quantile functional regression approaches that we are applying to model temporal climate change in terms of intraseasonal temperature and precipitation distributions.