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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Exploration of the use of Bayesian modeling of gradients for censored spatiotemporal data from the Deepwater Horizon oil spill
Spatial statistics, v 9(C), pp 166-179
Aug 2014
PMID: 25599019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper develops a hierarchical framework for identifying spatiotemporal patterns in data with a high degree of censoring using the gradient process. To do this, we impute censored values using a sampling-based inverse CDF method within our Markov chain Monte Carlo algorithm, thereby avoiding burdensome integration and facilitating efficient estimation of other model parameters. We illustrate use of our methodology using a simulated data example, and uncover the danger of simply substituting a space- and time-constant function of the level of detection for all missing values. We then fit our model to area measurement data of volatile organic compound (VOC) air concentrations collected on vessels supporting the response and clean-up efforts of the Deepwater Horizon oil release that occurred starting April 20, 2010. These data contained a high percentage of observations below the detectable limits of the measuring instrument. Despite this, we were still able to make some interesting discoveries, including elevated levels of VOC near the site of the oil well on June 26th. Using the results from this preliminary analysis, we hope to inform future research on the Deepwater Horizon study, including the use of gradient methods for assigning workers to exposure categories.
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Details
- Title
- Exploration of the use of Bayesian modeling of gradients for censored spatiotemporal data from the Deepwater Horizon oil spill
- Creators
- Harrison Quick - University of MissouriCaroline Groth - University of MinnesotaSudipto Banerjee - University of MinnesotaBradley P. Carlin - University of MinnesotaMark R. Stenzel - Product Innovation and Engineering (United States) (United States, Saint James) - LLCPatricia A. Stewart - Product Innovation and Engineering (United States) (United States, Saint James) - LLCDale P. Sandler - National Institute of Environmental Health SciencesLawrence S. Engel - University of North Carolina at Chapel HillRichard K. Kwok - National Institutes of Health
- Publication Details
- Spatial statistics, v 9(C), pp 166-179
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000346600800006
- Scopus ID
- 2-s2.0-84898518904
- Other Identifier
- 991020100193304721
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Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Geosciences, Multidisciplinary
- Mathematics, Interdisciplinary Applications
- Remote Sensing
- Statistics & Probability