The computational order of a DACE dynamical model |
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Authors: | Dorin Drignei |
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Affiliation: | Department of Mathematics and Statistics, Oakland University, Rochester, MI 48309, USA |
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Abstract: | Multivariate design and analysis of computer experiments (DACE) methodology can be useful in situations where a dynamical computer model produces time series data sets. The main result of this paper determines the computational order of prediction from a dynamical statistical model underpinned by the dynamical computer model. Furthermore, it is shown that the computational orders of predictions from this dynamical statistical model and from a black box statistical model are comparable, but the likelihood optimization of the former model is more efficient. A virus dynamics example shows that the dynamical statistical model predictions can be more accurate than both the black box statistical model predictions and a coarse numerical solution of similar computational order. |
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Keywords: | Computer experiment Multivariate normal distribution Local truncation error Ordinary differential system Statistical prediction |
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