Adaptive and robust experimental design for linear dynamical models using Kalman filter-Reference-Cited by-同舟云学术

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Adaptive and robust experimental design for linear dynamical models using Kalman filter

Published:2023-03-31 Issue:4 Volume:64 Page:1209-1231
ISSN:0932-5026
Container-title:Statistical Papers
language:en
Short-container-title:Stat Papers

Author:

Strouwen Arno^ORCID,Nicolaï Bart M.^ORCID,Goos Peter^ORCID

Funder

FWO

Onderzoeksraad, KU Leuven

Publisher

Springer Science and Business Media LLC

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://link.springer.com/content/pdf/10.1007/s00362-023-01438-9.pdf

Reference38 articles.

1. Anisimov VV, Fedorov VV, Leonov SL (2007) Optimal design of pharmacokinetic studies described by stochastic differential equations. In: mODa 8-advances in model-oriented design and analysis: proceedings of the 8th international workshop in model-oriented design and analysis held in Almagro, Spain, June 4–8, 2007. Springer, pp 9–16

2. Atkinson A, Donev A, Tobias R (2007) Optimum experimental designs, with SAS, vol 34. Oxford University Press, Oxford

3. Bezanson J, Edelman A, Karpinski S, Shah VB (2017) Julia: a fresh approach to numerical computing. SIAM Rev 59.1:65–98

4. Cavanaugh JE, Shumway RH (1996) On computing the expected Fisher information matrix for state-space model parameters. Stat Probab Lett 26.4:347–355

5. Chaloner K, Isabella V (1995) Bayesian experimental design: a review. Stat Sci 273–304

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