Semiparametric estimation of cross-covariance functions for multivariate random fields

The prevalence of spatially referenced multivariate data has impelled researchers to develop procedures for joint modeling of multiple spatial processes. This ordinarily involves modeling marginal and cross-process dependence for any arbitrary pair of locations using a multivariate spatial covariance function. However, building a flexible multivariate spatial covariance function that is nonnegative definite is challenging. Here, we propose a semiparametric approach for multivariate spatial covariance function estimation with approximate Matern marginals and highly flexible cross-covariance functions via their spectral representations. The flexibility in our cross-covariance function arises due to B-spline-based specification of the underlying coherence functions, which in turn allows us to capture nontrivial cross-spectral features. We then develop a likelihood-based estimation procedure and perform multiple simulation studies to demonstrate the performance of our method, especially on the coherence function estimation. Finally, we analyze particulate matter concentrations (PM2.5) and wind speed data over the West-North-Central climatic region of the United States, where we illustrate that our proposed method outperforms the commonly used full bivariate Matern model and the linear model of coregionalization for spatial prediction.

Automated summary

The paper proposes a semiparametric approach for estimating multivariate spatial covariance function, using spectral representations for approximate Matern marginals and highly flexible cross-covariance functions. The flexibility in cross-covariance function is achieved through specifying underlying coherence functions with B-splines, allowing capture of nontrivial cross-spectral features.