This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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183
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #306829 |
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Title:
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Spatial Statistical Data Fusion for Remote-Sensing Applications
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Author(s):
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Hai M. Nguyen*+ and Amy Braverman and Noel A. Cressie
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Companies:
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Jet Propulsion Lab and Jet Propulsion Lab and The Ohio State University
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Address:
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4800 Oak Grove Drive M/S 168-200, Pasadena, CA, 91011,
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Keywords:
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remote sensing ;
data fusion ;
kriging ;
aerosol
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Abstract:
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The use of remote sensing data in climate change studies promises many new scientific insights if those data can be efficiently exploited. Remote sensing data are often both sparse relative to geophysical processes of interest, and massive because their domains are so large. Combining complementary data from multiple sources can interpolate between gaps, and lead to more complete and accurate inferences, but only if massiveness can be overcome. We propose a linearly scalable, practical data fusion methodology to accomplish this. The method does not require assumptions of stationary or isotropy, and crucially allows for change of support. We demonstrate our methodology on data from two NASA instruments onboard the Terra spacecraft: MISR and MODIS.
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