Functional CAR models for large spatially correlated functional datasets.

ABSTRACT: We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on functions defined on higher dimensional domai

SUBMITTER: Zhang L 

PROVIDER: S-EPMC5176110 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature