Dorin Drignei

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Nonlinear regression is a useful statistical tool, relating observed data and a nonlinear function of unknown parameters. When the parameter-dependent nonlinear function is computationally intensive, a straightforward regression analysis by maximum likelihood is not feasible. The method presented in this paper proposes to construct a faster running(More)
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(More)
We propose a new metamodeling method to characterize the output (response) random process of a dynamic system with random parameters, excited by input random processes. The metamodel can be then used to efficiently estimate the time-dependent reliability of a dynamic system using analytical or simulation-based methods. The metamodel is constructed by(More)
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