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Research Spotlight
Sea surface temperatures (SST) anomalies observed in the
tropical Pacific in December 2002, compared to the HiDyn-Model
SST forecast based on data up to and including May 2002.
Details of the Bayesian HiDyn statistical model that gives
7-month forecasts can be found in L.M. Berliner, C.K. Wikle,
and N. Cressie (2000). "Long-lead prediction of Pacific SSTs
via Bayesian dynamic modeling". Journal of Climate, 13, 3953-
3968. A web-based tool that gives current and past forecasts
is available at the ENSO
page on the SSES
Program web-site.
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Predicted PM2.5 and PM10 concentration levels on three consecutive
days across the state of Ohio using a bivariate dynamic process
convolution model. This modeling approach explicitly takes into
account the additive nature of the components of particulate matter.
The spatial dependence structure of these latent components depends on
the size of the particles (small particles can travel farther than
large particles) and the direction and strength of the wind. This
work is described in C.A. Calder (2003), "A Bayesian Dynamic Process
Convolution Approach to Modeling PM2.5 and PM10 Concentration Levels,"
Department of Statistics Preprint No. 724, The Ohio State University.
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Total Column Ozone (TCO) world map for October 5, 1988, from
three different projections. The southern-polar projection (top
right) shows the ozone hole over Antarctica that is characteristic
of this region during the Southern-Hemisphere spring. The quantity
mapped is the predicted TCO (here the posterior expectation of TCO
given all the spatially irregular and incomplete data observed on
October 5, 1988). Details of the hierarchical multi-resolution
statistical model that gives the predicted values can be found in
G. Johannesson and N. Cressie (2004). "Variance-covariance modeling
and estimation for multi-resolution spatial models", in geoENV 2002
- Geostatistics for Environmental Applications, eds X. Sanchez-Vila,
J. Carrera, and J. Gomez-Hernandez. Kluwer, Dordrecht, forthcoming.
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Time series of plots of the predicted relative risk (RR) in the
56 health districts of Scotland during the height of an influenza
epidemic (Winter of 1989-1990). The "bubbles" are centered at the
district centroids and the area of each bubble is proportional to the
predicted RR (here the posterior median of the RR given all influenza
hospital admissions, by health district, during the epidemic). Details
of the Bayesian spatio-temporal model that gives the predicted values
can be found in A. Mugglin, N. Cressie, and I. Gemmell (2002).
"Hierarchical statistical modelling of influenza epidemic dynamics
in space and time". Statistics in Medicine, 21, 2703-2721.
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Multicategory Support Vector Machine Outputs for Cancer Classification
Dr. Yoonkyung Lee
MSVM decision vectors based on gene expression profiles
are plotted for 20 test cases of small round blue cell tumors (SRBCTs)
of childhood and five non SRBCT cases. Note that the decision vectors are
pretty close to their ideal class representation and they result in
correct classification for the 20 test cases. Details of this research can be found in Y. Lee and C.-K. Lee (2003)"Classification of Multiple Cancer Types by Multicategory Support Vector
Machines Using Gene Expression Data",Bioinformatics, vol. 19 (9),
1132-1139.
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Go to top Pictures on the right are directly linked to appropriate research page.
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Design and Analysis of Computer Experiments and its Application to Prosthesis Design
Dr. Thomas Santner
The tibial components of a prosthetic knee replacement include the
tibial tray (show with stem) and a fitted insert made from a high
molecular-weight polyethylene. The design of prosthetic knees and hips
is an interdisciplinary activity that can involve mechanical
engineers, material scientists, and orthopedic surgeons, and applied
statisticians. Statistical methodology allows researchers to evaluate
the mechanical properties of prospective prosthetic designs by
interpolating the mechanical properties for a test bed set of designs.
The choice of the test bed set of designs and the interpolation
process are statistical issues that are studied by statistical
researchers involved in the design and analysis of computer
experiments.
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With techniques which measures the time for the fastest-growing localized corrosion site
to penetrate foils of various thicknesses, it is found that the growth kinetics of localized corrosion of AA2024-T3
(a high strength aluminum alloy) exhibits a strong anisotropy. This effect occurs as pits initiated on the surface
transferred into intergranular corrosion and thus is the result of the microstructure of this type of alloy.
Statistic method is developed to model the relationship between the anisotropy of intergranular corrosion kinetics and
the microstructure of this alloy. Details of this research could be found in S. Ruan, D. A. Wolfe, W. Zhang, G. S. Frankel (2004)
"Statistical Modeling of Minimum Intergranular Corrosion Path Length in High-Strength Aluminum Alloy",
Technometrics , 46 (1) pp. 69-75(7). Extended work could be found in S. Ruan, D. A. Wolfe and G. S. Frankel (2004)
"Statistical modeling and computer simulation of intergranular corrosion growth in AA2024-T3 aluminum alloy",
Journal of Statistical Planning and Inference, 126(2),553-568.
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