1992 | OriginalPaper | Buchkapitel
Optimal Experimental Designs in Regression: A Bootstrap Approach
verfasst von : J. P. Vila
Erschienen in: Computational Statistics
Verlag: Physica-Verlag HD
Enthalten in: Professional Book Archive
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This paper presents a new family of optimal design criteria for parameter estimation in nonlinear regression, based on minimization of expected volumes of, at least second-order correct, bootstrap confidence regions.This approach relies on the bootstrap use of previous fitting information, and is free of any probability distribution hypothesis for the errors (except that they are centered and i.i.d.), while improving the classic first-order asymptotic approximation of D-optimality.