Robust and Efficient Designs for the Michaelis-Menten Model
Robust and Efficient Designs for the Michaelis-Menten Model
For the Michaelis-Menten model, we determine designs that maximize the minimum of the D-efficiencies over a certain interval for the nonlinear parameter. The best two point designs can be found explicitly, and a characterization is given when these designs are optimal within the class of all designs. In most cases of practical interest, the determined designs are highly efficient and robust with respect to misspecification of the nonlinear parameter. The results are illustrated and applied in an example of a hormone receptor assay.
Michaelis-Menten model, robust optimal design, local D-optimality, receptor assay
679-686
Dette, Holger
8c7b1c2e-3adc-45df-acfc-9e76509a228e
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
November 2003
Dette, Holger
8c7b1c2e-3adc-45df-acfc-9e76509a228e
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Dette, Holger and Biedermann, Stefanie
(2003)
Robust and Efficient Designs for the Michaelis-Menten Model.
Journal of the American Statistical Association, 98 (463), .
(doi:10.1198/016214503000000585).
Abstract
For the Michaelis-Menten model, we determine designs that maximize the minimum of the D-efficiencies over a certain interval for the nonlinear parameter. The best two point designs can be found explicitly, and a characterization is given when these designs are optimal within the class of all designs. In most cases of practical interest, the determined designs are highly efficient and robust with respect to misspecification of the nonlinear parameter. The results are illustrated and applied in an example of a hormone receptor assay.
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Published date: November 2003
Keywords:
Michaelis-Menten model, robust optimal design, local D-optimality, receptor assay
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Statistics
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Local EPrints ID: 41819
URI: http://eprints.soton.ac.uk/id/eprint/41819
ISSN: 0162-1459
PURE UUID: b6d7f50e-fe38-4a2d-99a8-7c303445f6c6
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Date deposited: 05 Oct 2006
Last modified: 16 Mar 2024 03:51
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Author:
Holger Dette
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