Abstract Details
Activity Number:
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602
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 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 - #309554 |
Title:
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A Variable Selection Technique for Detecting Climate Change Attribution
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Author(s):
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Siddhartha Nandy*+ and Chae Young Lim and Tapabrata Maiti
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Companies:
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Michigan State University and Michigan State and Michigan State University
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Keywords:
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Climate change and attribution ;
Spectral radiance change ;
Variable selection ;
Spatial additive models ;
Adaptive group LASSO
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Abstract:
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There is a considerable interest of developing sound quantitative and computationally feasible statistical tools suitable for analyzing spectral radiance change data to monitor the climate change and attributions. In this context, we developed a variable selection technique, specifically adaptive group LASSO type of selection, followed by a semiparametric additive model. We validate the method by simulation studies and applying to simulated spectral radiance measurement data for climate change. Theoretical validity will also be discussed.
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Authors who are presenting talks have a * after their name.
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