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Abstract Details

Activity Number: 335
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #306354
Title: Using Principal Component Functions to Model Spatially Dependent Functional Data
Author(s): Daniel Fortin*+ and Zhengyuan Zhu and Petrutza Caragea
Companies: and Iowa State University and Iowa State University
Address: 115 East 7th Street, Ames, IA, 50010, United States
Keywords: covariance function ; principal component function ; reproducing kernel Hilber space ; spatial dependence

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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