Optimal experimental design for linear time invariant state–space models

Duarte, Belmiro P.M., Atkinson, Anthony C. and Oliveira, Nuno M.C (2021) Optimal experimental design for linear time invariant state–space models. Statistics and Computing, 31 (4). ISSN 0960-3174

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Abstract

The linear time invariant state–space model representation is common to systems from several areas ranging from engineering to biochemistry. We address the problem of systematic optimal experimental design for this class of model. We consider two distinct scenarios: (i) steady-state model representations and (ii) dynamic models described by discrete-time representations. We use our approach to construct locally D–optimal designs by incorporating the calculation of the determinant of the Fisher Information Matrix and the parametric sensitivity computation in a Nonlinear Programming formulation. A global optimization solver handles the resulting numerical problem. The Fisher Information Matrix at convergence is used to determine model identifiability. We apply the methodology proposed to find approximate and exact optimal experimental designs for static and dynamic experiments for models representing a biochemical reaction network where the experimental purpose is to estimate kinetic constants.

Item Type:	Article
Official URL:	https://www.springer.com/journal/11222
Additional Information:	© 2021 The Authors, under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	01 Jun 2021 09:36
Last Modified:	28 Mar 2024 00:30
URI:	http://eprints.lse.ac.uk/id/eprint/110735

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