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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Friday, October 18, 2002}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Sandra McBride}
\centerline{\sl Duke University}
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\centerline{\bf Hierarchical Bayesian calibration: An application to airborne particulate matter monitoring data}
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In studies of the relationship betweeen airborne fine particulate matter
(PM2.5) and health, researchers frequently use monitoring data with the
most extensive temporal coverage, even if such data may come from a
monitor that has not met U.S. Environmental Protection Agency (USEPA)
reference monitor standards.  For the Phoenix area, measurements from a
"gold standard" reference monitor are available less frequently and have
levels of accuracy and bias that differ from a co-located non-reference
monitor.  Using the soil constituent of PM2.5 as an illustration, we
describe a Bayesian hierarchical model that combines information from
reference and non-reference monitors to produce a temporally resolved
estimate of the reference concentration time series as well as the
unknown
mean concentration time series.  Mean concentrations are modeled using a
regression structure that reflects the influence of meteorology.  To
account for bias in monitors relative to each other, a multiplicative
bias
parameter in the mean for the non-reference monitor is used.  Estimation
of the bias parameter involves inference about the ratio of normal means
as in the well-known Fieller-Creasy problem.  We develop a reference
prior
for the hierarchical model that permits simultaneous inference about the
underlying mean concentrations and the bias parameter.  For this case
study, we describe the implications of using non-reference monitoring
data
in models relating PM2.5 and health.  This work is joint with Merlise
Clyde, Associate Professor in ISDS, and Allan Marcus, USEPA.


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