  Continuous random processes and fields are regularly applied to model
temporal or spatial phenomena in many different fields of science, and model
fitting is usually done with the help of data obtained by observing the given
process at various time points or spatial locations. In these practical
applications sampling designs which are optimal in some sense are of great
importance. We investigate the properties of the recently introduced K-optimal
design for temporal and spatial linear regression models driven by
Ornstein-Uhlenbeck processes and sheets, respectively, and highlight the
differences compared with the classical D-optimal sampling. A simulation study
displays the superiority of the K-optimal design for large parameter values of
the driving random process.
