Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation

Abstract

SIAM/ASA Journal on Uncertainty Quantification, Volume 11, Issue 4, Page 1225-1257, December 2023.
Abstract. It is common to model a deterministic response function, such as the output of a computer experiment, as a Gaussian process with a Matérn covariance kernel. The smoothness parameter of a Matérn kernel determines many important properties of the model in the large data limit, including the rate of convergence of the conditional mean to the response function. We prove that the maximum likelihood estimate of the smoothness parameter cannot asymptotically undersmooth the truth when the data are obtained on a fixed bounded subset of [math]. That is, if the data-generating response function has Sobolev smoothness [math], then the smoothness parameter estimate cannot be asymptotically less than [math]. The lower bound is sharp. Additionally, we show that maximum likelihood estimation recovers the true smoothness for a class of compactly supported self-similar functions. For cross-validation we prove an asymptotic lower bound [math], which, however, is unlikely to be sharp. The results are based on approximation theory in Sobolev spaces and some general theorems that restrict the set of values that the parameter estimators can take.