Structure of Optimal Samples in Continuous Nonlinear Experimental Design for Parameter Estimation

H. C. La (), H. G. Bock () and J. P. Schlöder ()
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H. C. La: Interdisciplinary Center for Scientific Computing (IWR)
H. G. Bock: Interdisciplinary Center for Scientific Computing (IWR)
J. P. Schlöder: Interdisciplinary Center for Scientific Computing (IWR)

A chapter in Modeling, Simulation and Optimization of Complex Processes HPSC 2015, 2017, pp 81-91 from Springer

Abstract: Abstract In the continuous case, Optimal Experimental Design (OED) deals with designs that are described by probability distributions or samples over the experimental domain. An optimal design may correspond to a distribution having finite or infinite support or being continuous. In this paper, the structure of optimal samples for experimental designs is elucidated. It is shown that any design is in fact equivalent to a design with a finite number of support points. The lower bound and upper bound of this number, especially for optimal designs, are given and examples indicate their sharpness. Moreover, we propose an algorithm to construct optimal designs which have finite support. Several applications to OED for dynamic systems with inputs are also discussed.

Keywords: Support Points; Optimal Experimental Design (OED); Finite Support; Experimental Domain; Discrete Design (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-67168-0_7

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DOI: 10.1007/978-3-319-67168-0_7

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