The Gauss hypergeometric covariance kernel for modeling second-order stationary random fields in Euclidean spaces: its compact support, properties and spectral representation

This paper presents a parametric family of compactly-supported positive semidefinite kernels aimed to model the covariance structure of second-order stationary isotropic random fields defined in the d-dimensional Euclidean space. Both the covariance and its spectral density have an analytic expression involving the hypergeometric functions F-2(1) and F-1(2), respectively, and four real-valued parameters related to the correlation range, smoothness and shape of the covariance. The presented hypergeometric kernel family contains, as special cases, the spherical, cubic, penta, Askey, generalized Wendland and truncated power covariances and, as asymptotic cases, the Matern, Laguerre, Tricomi, incomplete gamma and Gaussian covariances, among others. The parameter space of the univariate hypergeometric kernel is identified and its functional properties-continuity, smoothness, transitive upscaling (montee) and downscaling (descente)-are examined. Several sets of sufficient conditions are also derived to obtain valid stationary bivariate and multivariate covariance kernels, characterized by four matrix-valued parameters. Such kernels turn out to be versatile, insofar as the direct and cross-covariances do not necessarily have the same shapes, correlation ranges or behaviors at short scale, thus associated with vector random fields whose components are cross-correlated but have different spatial structures.

Automated summary

This paper introduces a parametric family of compactly-supported positive semidefinite kernels for modeling the covariance structure of second-order stationary isotropic random fields in d-dimensional Euclidean space. The kernels have analytic expressions involving hypergeometric functions and real-valued parameters. Various specific and asymptotic cases are included in the kernel family, and the properties and conditions of the kernels are examined for univariate, bivariate, and multivariate cases.