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Abstract Details

Activity Number: 303
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract - #305745
Title: A Multivariate Spatial Modeling Approach Using Semiparametric Covariance Functions
Author(s): Yong Wang*+ and Hao Zhang
Companies: Eastern Kentucky University and Purdue University
Address: 101B Eastern Hills Drive, Richmond, KY, 40475, United States
Keywords: Multivariate spatial model ; covariogram ; nonparametric

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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