Fixed-Domain Posterior Contraction Rates for Spatial Gaussian Process Model with Nugget

Spatial Gaussian process regression models typically contain finite dimensional covariance parameters that need to be estimated from the data. We study the Bayesian estimation of covariance parameters including the nugget parameter in a general class of stationary covariance functions under fixed-domain asymptotics, which is theoretically challenging due to the increasingly strong dependence among spatial observations. We propose a novel adaptation of the Schwartz's consistency theorem for showing posterior contraction rates of the covariance parameters including the nugget. We derive a new polynomial evidence lower bound, and propose consistent higher-order quadratic variation estimators that satisfy concentration inequalities with exponentially small tails. Our Bayesian fixed-domain asymptotics theory leads to explicit posterior contraction rates for the microergodic and nugget parameters in the isotropic Matern covariance function under a general stratified sampling design. We verify our theory and the Bayesian predictive performance in simulation studies and an application to sea surface temperature data. for this article are available online.

Automated summary

This article investigates the estimation of finite-dimensional covariance parameters in spatial Gaussian process regression models. We propose a Bayesian estimation approach under fixed-domain asymptotics and a novel adaptation of Schwartz's consistency theorem for showing posterior contraction rates of the covariance parameters. We derive a new polynomial evidence lower bound and propose consistent higher-order quadratic variation estimators. Our theory provides explicit posterior contraction rates for the microergodic and nugget parameters in the isotropic Matern covariance function under a general stratified sampling design. Simulation studies and an application to sea surface temperature data validate our theory and Bayesian predictive performance.