An approach to modeling asymmetric multivariate spatial covariance structures

An approach to modeling asymmetric multivariate spatial covariance structures

Nonstationary Multivariate Spatial Covariance Modeling

We derive a class of matrix valued covariance functions where the direct and crosscovariance functions are Matérn. The parameters of the Matérn class are allowed to vary with location, yielding local variances, local ranges, local geometric anisotropies and local smoothnesses. We discuss inclusion of a nonconstant cross-correlation coefficient and a valid approximation. Estimation utilizes kern...

Multivariate Spatial Covariance Models: A Conditional Approach

Modeling spatial covariance functions

Choi, InKyung Ph.D., Purdue University, December 2014. Modeling spatial covariance functions. Major Professor: Hao Zhang. Covariance modeling plays a key role in the spatial data analysis as it provides important information about the dependence structure of underlying processes and determines performance of spatial prediction. Various parametric models have been developed to accommodate the id...

Covariance Approximation for Large Multivariate Spatial Datasets with an Application to Multiple Climate Model Errors

This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a non-separable and non-stationary cross-covariance structure. We also present a covariance approximation approach to facilitat...