کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1064605 1485790 2014 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Exploration of the use of Bayesian modeling of gradients for censored spatiotemporal data from the Deepwater Horizon oil spill
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات علوم زمین و سیاره ای (عمومی)
پیش نمایش صفحه اول مقاله
Exploration of the use of Bayesian modeling of gradients for censored spatiotemporal data from the Deepwater Horizon oil spill
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Spatial Statistics - Volume 9, August 2014, Pages 166–179
نویسندگان
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