Abstract
In Computer Experiments (CE), a careful selection of the design points is essential for predicting the system response at untried points, based on the values observed at tried points. In physical experiments, the protocol is based on Design of Experiments, a methodology whose basic principles are questioned in CE. When the responses of a CE are modeled as jointly Gaussian random variables with their covariance depending on the distance between points, the use of the so called space-filling designs (random designs, stratified designs and Latin Hypercube designs) is a common choice, because it is expected that the nearer the untried point is to the design points, the better is the prediction. In this paper we focus on the class of Latin Hypercube (LH) designs. The behavior of various LH designs is examined according to the Gaussian assumption with exponential correlation, in order to minimize the total prediction error at the points of a regular lattice. In such a special case, the problem is reduced to an algebraic statistical model, which is solved using both symbolic algebraic software and statistical software. We provide closed-form computation of the variance of the Gaussian linear predictor as a function of the design, in order to make a comparison between LH designs. In principle, the method applies to any number of factors and any number of levels, and also to classes of designs other than LHs. In our current implementation, the applicability is limited by the high computational complexity of the algorithms involved.
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The first author acknowledges the support of DIMAT Politecnico di Torino and of SAMSI, NC, he visited Jan-Mar 2009, where the computational part was developed and some issues were discussed in a working group of the 2008-09 SAMSI Program on Algebraic Methods in Systems Biology and Statistics. The second Author acknowledges the support of DISPEA Politecnico di Torino.
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Pistone, G., Vicario, G. Comparing and generating Latin Hypercube designs in Kriging models. AStA Adv Stat Anal 94, 353–366 (2010). https://doi.org/10.1007/s10182-010-0142-1
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DOI: https://doi.org/10.1007/s10182-010-0142-1