Estimating structured correlation matrices in smooth Gaussian random field models

Abstract

This article considers the estimation of structured correlation matrices in infinitely differentiable Gaussian random field models. The problem is essentially motivated by the stochastic modeling of smooth deterministic responses in computer experiments. In particular, the log-likelihood function is determined explicitly in closed-form and the sieve maximum likelihood estimators are shown to be strongly consistent under mild conditions.