Print Email Facebook Twitter Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis Title Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis Author Datta, A. Banerjee, S. Finley, A.O. Hamm, N.A.S. Schaap, M. Publication year 2016 Abstract Particulate matter (PM) is a class of malicious environmental pollutants known to be detrimental to human health. Regulatory efforts aimed at curbing PM levels in different countries often require high resolution space–time maps that can identify red-flag regions exceeding statutory concentration limits. Continuous spatio-temporal Gaussian Process (GP) models can deliver maps depicting predicted PM levels and quantify predictive uncertainty. However, GP-based approaches are usually thwarted by computational challenges posed by large datasets. We construct a novel class of scalable Dynamic Nearest Neighbor Gaussian Process (DNNGP) models that can provide a sparse approximation to any spatio-temporal GP (e.g., with nonseparable covariance structures). The DNNGP we develop here can be used as a sparsity-inducing prior for spatio-temporal random effects in any Bayesian hierarchical model to deliver full posterior inference. Storage and memory requirements for a DNNGP model are linear in the size of the dataset, thereby delivering massive scalability without sacrificing inferential richness. Extensive numerical studies reveal that the DNNGP provides substantially superior approximations to the underlying process than low-rank approximations. Finally, we use the DNNGP to analyze a massive air quality dataset to substantially improve predictions of PM levels across Europe in conjunction with the LOTOS-EUROS chemistry transport models (CTMs). © Institute of Mathematical Statistics, 2016. Subject Urban Mobility & EnvironmentCAS - Climate, Air and SustainabilityELSS - Earth, Life and Social SciencesEnvironment & SustainabilityEnvironmentUrbanisationBayesian inferenceEnvironmental pollutantsMarkov chain Monte CarloNearest neighborsNonseparable spatio-temporal modelsScalable Gaussian process To reference this document use: http://resolver.tudelft.nl/uuid:97384811-405c-47d1-b79e-4a643946ee6c DOI https://doi.org/10.1214/16-aoas931 TNO identifier 573285 Publisher Institute of Mathematical Statistics ISSN 1932-6157 Source Annals of Applied Statistics, 10 (3), 1286-1316 Bibliographical note Funding Details: NASA, National Aeronautics and Space Administration Funding Details: DMS-15-13654, NSF, National Science Foundation Funding Details: DMS-1513481, NSF, National Science Foundation Funding Details: EF-1137309, NSF, National Science Foundation Funding Details: EF-1241874, NSF, National Science Foundation Funding Details: EF-1253225, NSF, National Science Foundation Funding Details: R01OH010093, NIOSH, National Institute for Occupational Safety and Health Document type article Files To receive the publication files, please send an e-mail request to TNO Library.