The Annals of Applied Statistics

Nonlinear predictive latent process models for integrating spatio-temporal exposure data from multiple sources

Nikolay Bliznyuk, Christopher J. Paciorek, Joel Schwartz, and Brent Coull

Full-text: Open access

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 computationally prohibitive. The implications are particularly severe when the data for each submodel are aggregated at different temporal scales.

To make such problems tractable, we linearize the nonlinear components with respect to the latent process and induce sparsity in the covariance matrix of the latent process using compactly supported covariance functions. We propose an efficient MCMC scheme that takes advantage of these approximations. We use our model to address a temporal change of support problem whereby interest focuses on pooling daily and multiday black carbon readings in order to maximize the spatial coverage of the study region.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 3 (2014), 1538-1560.

Dates
First available in Project Euclid: 23 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1414091224

Digital Object Identifier
doi:10.1214/14-AOAS737

Mathematical Reviews number (MathSciNet)
MR3271343

Zentralblatt MATH identifier
1304.62141

Keywords
Air pollution approximate inference covariance tapering Gaussian processes hierarchical model likelihood approximation particulate matter semiparametric model spatio-temporal model

Citation

Bliznyuk, Nikolay; Paciorek, Christopher J.; Schwartz, Joel; Coull, Brent. Nonlinear predictive latent process models for integrating spatio-temporal exposure data from multiple sources. Ann. Appl. Stat. 8 (2014), no. 3, 1538--1560. doi:10.1214/14-AOAS737. https://projecteuclid.org/euclid.aoas/1414091224


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Supplemental materials

  • Supplementary material: Supplement to “Nonlinear predictive latent process models for integrating spatio-temporal exposure data from multiple sources”. Online supplements contain technical details and supplementary figures and tables.