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Activity Number:
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399
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Type:
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Contributed
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Date/Time:
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Thursday, August 15, 2002 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics & the Environment*
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| Abstract - #301539 |
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Title:
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A Hierarchical Model of Fluoride in U.S. Drinking Water
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Author(s):
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Song Qian*+ and Jonathan Koplos and Andrew Schulman
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Affiliation(s):
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The Cadmus Group, Inc. and The Cadmus Group, Inc. and U.S. Environmental Protection Agency
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Address:
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1920 Highway 54, Suit 100, Durham, North Carolina, 27713, USA
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Keywords:
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Bayesian ; left censored data ; MCMC ; risk ; safe drinking water act
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Abstract:
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Water quality studies often include the analytical challenge of incorporating censored data and quantifying error of estimation. Many analytical methods exist for estimating distribution parameters when censored data are present. This paper presents a Bayesian-based hierarchical model for estimating the national distribution of the mean fluoride concentrations in US public drinking water systems. The data used are Safe Drinking Water Act compliance-monitoring data (with a significant proportion of left-censored data). The model, which assumes log-normality on the fluoride concentration data, was evaluated using simulated data sets generated from a series of Weibull distributions to illustrate the robustness of the model. When compared to the regression on ordered statistics (ROS) method (another modeling approach used to handle censored data), the Bayesian model tracks the true distribution well, while the ROS plotting position method yielded a distribution with a slightly larger variance and smaller median. In addition, the Bayesian method is able to quantify the uncertainty in the estimated CDF. The estimated national fluoride distribution is presented.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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