Large Sample Properties of ML Estimator of the Parameters of Multivariate O–U Random Fields

Computer modeling is having a profound effect on scientific research, replacing direct physical experimentation by computer simulation of complex models. In this research, the computer output, X(t), is assumed to be a multivariate, three-dimensional (time) Ornstein-Uhlenbeck (O–U) process with parametric covariance function. It is shown that the ML estimates of the parameters are strongly consistent and asymptotically normal when the observations are taken from a complete lattice, not necessarily equally spaced.