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Abstract Details
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Activity Number:
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249
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #302446 |
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Title:
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Lognormal Block Kriging for Multivariate Spatial Processes
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Author(s):
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Mona Abdullah Alduailij*+ and Rajib Paul
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Companies:
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Western Michigan University and Western Michigan University
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Address:
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4435 Wimbleton way, Kalamazoo, MI, 49009,
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Keywords:
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Cross-covariance ;
Dimension reduction ;
Nonstationarity ;
Remote-Sensing
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Abstract:
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Remote-Sensing datasets obtained from satellite are very large and often require dimension reduction techniques for statistical analysis. We analyze datasets on three cloud properties. (i) Cloud Optical Thickness, (ii) Cloud Top Pressure, and (iii) Cloud Water Path, obtained from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). These datasets exhibit skewness and log-transformation provides numerical stability. We derive the optimal predictor for the unobserved locations after taking into consideration the nonstationarity, spatial and cross-covariances of these multivariate spatial processes. We also produce maps for the root-mean squared prediction errors to assess the goodness of our predictions.
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