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- W62387746 abstract "In this paper we discuss the structure of the information matrices of D-optimal experimental designs for the parameters in a stationary process when the parametrized correlation structure satisfies mild conditions. Such conditions are easily fulfilled by many correlation structures, e.g. structures from power exponential family and some members of the Matérn class. We provide a lower bound for information on the mean parameter and prove it to be an increasing function of distances of design points. The design points can collapse under the presence of some covariance structures and a so called nugget effect can be employed in a natural way. We also show that the information of equidistant designs (designs with equally spaced design points) on the covariance parameter is increasing with the number of design points under our conditions on correlations. If only trend parameters are of interest, the designs covering the whole design space non-uniformly are rather efficient." @default.
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- W62387746 date "2007-05-11" @default.
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- W62387746 title "D-optimal Designs and Equidistant Designs for Stationary Processes" @default.
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- W62387746 doi "https://doi.org/10.1007/978-3-7908-1952-6_26" @default.
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