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W2319057860 abstract "Abstract In prediction error identification model uncertainty bounds are generally derived from the statistical properties of the parameter estimator, i.e. asymptotic normal distribution of the estimator, and availability of the covariance information. When the primal interest of the identification is in a-posteriori quantifying the uncertainty in an estimated parameter, alternative parameter confidence bounds can be constructed. Probabilistic parameter confidence bounds are studied for ARX models which are generated by computationally more simple expressions, and which have the potential of being less dependent on asymptotic approximations and assumptions. It is illustrated that the alternative bounds can be powerful for quantifying parameter confidence regions for finite-time situations." @default.
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W2319057860 date "2006-01-01" @default.
W2319057860 modified "2023-10-18" @default.
W2319057860 title "PROBABILISTIC MODEL UNCERTAINTY BOUNDING: AN APPROACH WITH FINITE-TIME PERSPECTIVES" @default.
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W2319057860 doi "https://doi.org/10.3182/20060329-3-au-2901.00163" @default.
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