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Multivariate Spatial Process Models

  • Alan E. GelfandEmail author
Living reference work entry

Abstract

Spatially-referenced multivariate data is becoming increasingly common. Here, we focus on data in the form of vectors observed at a finite set of spatial locations. Regression models are of interest in order to explain the response vectors as well as to predict response at unobserved locations. Such models need to capture both dependence among the components of the vectors as well as spatial dependence across the locations of the vectors. The objective of this chapter is to develop classes of models which provide the desired dependence. We consider this both formally and constructively. Constructive development is supplied through four different strategies. An example, using soil nutrient data from Costa Rica is presented.

Keywords

Convolution Co-regionalization Cross-covariance Cross-variogram Gausspian process Matérn covariance Separability 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Statistical ScienceDuke UniversityDurhamUSA

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