Authors: | Konstantinou, Maria Dette, Holger |
Title: | Bayesian D-optimal designs for error-in-variables models |
Language (ISO): | en |
Abstract: | Bayesian optimality criteria provide a robust design strategy to parameter misspeci- fication. We develop an approximate design theory for Bayesian D-optimality for non- linear regression models with covariates subject to measurement errors. Both maximum likelihood and least squares estimation are studied and explicit characterisations of the Bayesian D-optimal saturated designs for the Michaelis-Menten, Emax and exponential regression models are provided. Several data examples are considered for the case of no preference for specific parameter values, where Bayesian D-optimal saturated designs are calculated using the uniform prior and compared to several other designs, including the corresponding locally D-optimal designs, which are often used in practice. |
Subject Headings: | error-in-variables models D-optimality Bayesian optimal designs classical errors |
URI: | http://hdl.handle.net/2003/34966 http://dx.doi.org/10.17877/DE290R-17014 |
Issue Date: | 2016 |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_2416_SFB823_Konstantinou_Dette.pdf | DNB | 310.72 kB | Adobe PDF | View/Open |
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