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Activity Number:
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59
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #304342 |
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Title:
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Spatial Temporal Data Analysis via Reproducing Kernel Regularization
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Author(s):
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Javier González Hernández*+ and Alberto Muñoz García and Stephan R. Sain
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Companies:
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Universidad Carlos III de Madrid and Universidad Carlos III de Madrid and National Center for Atmospheric Research
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Address:
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C/ Madrid, 126 , Getafe Madrid, 28903, Spain
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Keywords:
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Spatial Temporal Processes ; Regularization Theory ; Reproducing Kernel Hilbert Spaces
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Abstract:
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Spatial temporal processes data are difficult to manage and they usually exhibit a complex interaction between the spatial and temporal components. In this work we propose a methodology to deal with these iterations by considering the sample spatial temporal data process a as a point in a general function space and then to project it onto a Reproducing Kernel Hilbert Space (RKHS) with the aid of Regularization theory. We explore the performance of this methodology when the covariance function is defined in several ways (analyzing stationary and separability) and we illustrate its performance in some real examples.
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