Multivariate spatial covariance models: a conditional approach

Multivariate Spatial Covariance Models: A Conditional Approach

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...

An approach to modeling asymmetric multivariate spatial covariance structures

Proper multivariate conditional autoregressive models for spatial data analysis.

In the past decade conditional autoregressive modelling specifications have found considerable application for the analysis of spatial data. Nearly all of this work is done in the univariate case and employs an improper specification. Our contribution here is to move to multivariate conditional autoregressive models and to provide rich, flexible classes which yield proper distributions. Our app...

A conditional approach for multivariate extreme values

Multivariate extreme value theory and methods concern the characterization, estimation and extrapolation of the joint tail of the distribution of a d-dimensional random variable. Existing approaches are based on limiting arguments in which all components of the variable become large at the same rate.This limit approach is inappropriate when the extreme values of all the variables are unlikely t...