Nonlinear predictive latent process models for integrating spatio-temporal exposure data from multiple sources
DOI10.1214/14-AOAS737zbMath1304.62141arXiv1411.3479OpenAlexW2007910870WikidataQ55035313 ScholiaQ55035313MaRDI QIDQ484017
Nikolay Bliznyuk, Joel Schwartz, Christopher J. Paciorek, Brent A. Coull
Publication date: 17 December 2014
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1411.3479
Gaussian processesair pollutionsemiparametric modelhierarchical modelapproximate inferencespatio-temporal modelcovariance taperinglikelihood approximationparticulate matter
Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to environmental and related topics (62P12)
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