Evaluation of Scan Methods Used in the Monitoring of Public Health Surveillance

Shannon Elizabeth Fraker

Abstract
With the recent increase in the threat of biological terrorism as well as the continual risk
of other diseases, the research in public health surveillance and disease monitoring has grown
tremendously. There is an abundance of data available in all sorts of forms. Hospitals, federal
and local governments, and industries are all collecting data and developing new methods to be
used in the detection of anomalies. Many of these methods are developed, applied to a real data
set, and incorporated into software. This research, however, takes a different view of the
evaluation of these methods.

Keywords: Anomaly detection, CUSUM charts, EWMA charts, RecurrenceInterval, Scan Method, Time-to-SignalCopyright 2007.

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Least Squares Optimal Scalingof Partially Observed Linear Systems

JAN DE LEEUW

Abstract
We study linear systems in which both the coefficients ofthe linear combinations and the variables which are combined linearlyare only partially known. This includes the two logical extremes com-pletely known and completely unknown. The systems we study includethe usual linear systems of simultaneous equations, as well as nonlin-ear multivariate analysis systems. Throughout, we use unweighted leastsquares loss functions and majorization algorithms to minimize them.We incorporate both errors-in-equations and errors-in-variables as ad-ditional unknowns into the loss function, and arrive at algorithms fordecompositions of partially unknown matrices.

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Bayesian Modeling of Accelerated Life Tests with Random Effects

By
Avery J. Ashby
Ramón V. León
Jayanth Thyagarajan
Department of Statistics, The University of Tennessee, Knoxville. 37996-0532

Abstract
We show how to use Bayesian modeling to analyze data from an accelerated life test
where the test units come from different groups (such as batches) and the group effect is
random and significant. Our approach can handle multiple random effects and several
accelerating factors. However, we present our approach on the basis on an important
application concerning pressure vessels wrapped in Kevlar 49 fibers where the fibers of
each vessel comes from a single spool and the spool effect is random. We show how
Bayesian modeling using Markov chain Monte Carlo (MCMC) methods can be used to
easily answer questions of interest in accelerated life tests with random effects that are
not easily answered with more traditional methods. For example, we can predict the
lifetime of a pressure vessel wound with a Kevlar 49 fiber either from a spool used in the
accelerated life test or from another random spool from the population of spools. We
comment on the implications that this analysis has on the estimates of reliability (and
safety) for the Space Shuttle, which has a system of 22 such pressure vessels. Our
2 approach is implemented in the freely available WinBUGS software so that readers can
easily apply the method to their own data.

Keywords: Markov chain Monte Carlo (MCMC), WinBUGS, Credibility Interval,
Prediction Interval, Quantile.

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