Optimal Design Criteria for Discrimination and Estimation in Nonlinear Models

Waterhouse, T. H., Eccleston, J. A. and Duffull, S. B. (2009) Optimal Design Criteria for Discrimination and Estimation in Nonlinear Models. Journal of Biopharmaceutical Statistics, 19 2: 386-402. doi:10.1080/10543400802677257


Author Waterhouse, T. H.
Eccleston, J. A.
Duffull, S. B.
Title Optimal Design Criteria for Discrimination and Estimation in Nonlinear Models
Journal name Journal of Biopharmaceutical Statistics   Check publisher's open access policy
ISSN 1054-3406
Publication date 2009-01-01
Sub-type Article (original research)
DOI 10.1080/10543400802677257
Open Access Status Not yet assessed
Volume 19
Issue 2
Start page 386
End page 402
Total pages 17
Editor S.-C. Chow
Place of publication United States
Publisher Taylor & Francis
Language eng
Subject C1
970101 Expanding Knowledge in the Mathematical Sciences
010405 Statistical Theory
Abstract Nonlinear models are common in pharmacokinetics and pharmacodynamics. To date, most work in design in this area has concentrated on parameter estimation. Here, we introduce the idea of optimization of both estimation and model selection. However, experimental designs that provide powerful discrimination between a pair of competing model structures are rarely efficient in terms of estimating the parameters under each model. Conversely, designs which are efficient for parameter estimation may not provide suitable power to discriminate between the models. Several different methods of addressing both of these objectives simultaneously are introduced in this paper and are compared to an existing optimality criterion.
Keyword Conditional optimality
Discrimination
Hybrid design
Nonlinear model
Optimal design
Parameter estimation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
2010 Higher Education Research Data Collection
 
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Created: Thu, 06 Aug 2009, 00:40:27 EST by Marie Grove on behalf of School of Mathematics & Physics