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Abstract Details
Activity Number:
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303
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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Abstract - #305745 |
Title:
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A Multivariate Spatial Modeling Approach Using Semiparametric Covariance Functions
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Author(s):
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Yong Wang*+ and Hao Zhang
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Companies:
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Eastern Kentucky University and Purdue University
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Address:
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101B Eastern Hills Drive, Richmond, KY, 40475, United States
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Keywords:
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Multivariate spatial model ;
covariogram ;
nonparametric
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Abstract:
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Building a multivariate spatial model is a challenging task since it involves a great amount of work in terms of formulation and computation. Most existing multivariate spatial models take a parametric approach and as a result are limited in their applicability. We propose a semi-parametric approach to multivariate spatial modeling which offers great flexibility and much improved predictive performance. This modeling approach uses parametric covariograms in the marginal models and a non-parametric cross-covariogram. A simple assumption of the cross-covariance structure guarantees that the resulting multivariate covariogram is positive definite. The non-parametric feature of the cross-covariogram gives this modeling approach great flexibility to accommodate any form of marginal covariogram. Also, this approach has demonstrated superior predictive performance over several popular existing multivariate spatial models through a series of simulation and real data examples.
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