NONLINEAR PREDICTIVE LATENT PROCESS MODELS FOR INTEGRATING SPATIO-TEMPORAL EXPOSURE DATA FROM MULTIPLE SOURCES.

ABSTRACT: Spatio-temporal prediction of levels of an environmental exposure is an important problem in environmental epidemiology. Our work is motivated by multiple studies on the spatio-temporal distribution of mobile source, or traffic related, particles in the greater Boston area. When multiple sources of exposure information are available, a joint model that pools information across sources maximizes data coverage over both space and time, thereby reducing the prediction error. We consider a Bayesian hierarchical framework in which a joint model consists of a set of submodels, one for each data source, and a model for the latent process that serves to relate the submodels to one another. If a submodel depends on the latent process nonlinearly, inference using standard MCMC techniques can be comp

SUBMITTER: Bliznyuk N 

PROVIDER: S-EPMC5983907 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature