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Abstract Details
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Activity Number:
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335
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #306354 |
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Title:
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Using Principal Component Functions to Model Spatially Dependent Functional Data
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Author(s):
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Daniel Fortin*+ and Zhengyuan Zhu and Petrutza Caragea
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Companies:
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and Iowa State University and Iowa State University
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Address:
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115 East 7th Street, Ames, IA, 50010, United States
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Keywords:
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covariance function ;
principal component function ;
reproducing kernel Hilber space ;
spatial dependence
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Abstract:
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In functional data analysis it is often of interest to estimate the principal component functions. A recently proposed method for nonparametric covariance function estimation using a reproducing kernel Hilbert space framework allows for closed form estimates of the principal component functions, thus alleviating the need for discretization and numerical approximation. We discuss this approach and develop a spatial model for functional data where the curves are represented by a principal component function basis and spatial dependence is modelled through the coefficients.
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