NDLI: Geostatistical hierarchical model for temporally integrated radon measurements

Content Provider	Springer Nature Link
Author	Smith, Brian J. Oleson, Jacob J.
Copyright Year	2008
Abstract	In the search for important determinants of disease, epidemiologists often face the challenging task of retrospectively estimating exposures of interest. Such is the case in modern studies of the lung cancer risk posed by residential radon—a naturally occurring radioactive gas. Assessment of past radon exposures is limited because measurements are not generally available for the locations at which study subjects spent time prior to enrollment. In such settings, there is a need for prediction at unmeasured geopraphic sites and time periods. We develop a hierarchical Bayesian goestatistical model for predicting unmeasured radon concentrations over space and time. Our work arises from a study of residential radon in Iowa, where measurements were taken as yearly averages and subject to detector measurement error. Much attention has been given lately to geostatistical methods for data that are obtained as integrated averages over geographic regions. We show how these techniques work in the time domain as well. Unlike the numerical approximations that are needed to integrate over geographic regions, we are able to provide closed-form solutions for the integration that must be performed over temporal periods. Our approach is illustrated with radon concentrations measured from 614 different geographic sites and 799 time periods.
Starting Page	140
Ending Page	158
Page Count	19
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	13
Issue Number	2
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian methods Environmental exposure assessment Markov chain Monte Carlo Residential radon Spatio-temporal data Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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