Hierarchical factor models for large spatially misaligned data: a low-rank predictive process approach.

ABSTRACT: This article deals with jointly modeling a large number of geographically referenced outcomes observed over a very large number of locations. We seek to capture associations among the variables as well as the strength of spatial association for each variable. In addition, we reckon with the common setting where not all the variables have been observed over all locations, which leads to spatial misalignment. Dimension reduction is needed in two aspects: (i) the length of the vector of outcomes, and (ii) the very large number of spatial locations. Latent variable (factor) models are usually used to address the former, although low-rank spatial processes offer a rich and flexible modeling option for dealing with a large number of locations. We merge these two ideas to propose a class of hiera

SUBMITTER: Ren Q 

PROVIDER: S-EPMC4466112 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature