Convergence Arguments to Bridge Cauchy and Matérn Covariance Functions

The Mat\érn and the Generalized Cauchy families of covariance functions have a prominent role in spatial statistics as well as in a wealth of statistical applications. The Mat\érn family is crucial to index mean-square differentiability of the associated Gaussian random field; the Cauchy family is a decoupler of the fractal dimension and Hurst effect for Gaussian random fields that are not self-similar. Our effort is devoted to prove that a scale-dependent family of covariance functions, obtained as a reparameterization of the Generalized Cauchy family, converges to a particular case of the Mat\érn family, providing a somewhat surprising bridge between covariance models with light tails and covariance models that allow for long memory effect.