Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Konstantinou, Maria | - |
dc.contributor.author | Dette, Holger | - |
dc.date.accessioned | 2016-05-18T10:31:47Z | - |
dc.date.available | 2016-05-18T10:31:47Z | - |
dc.date.issued | 2016 | - |
dc.identifier.uri | http://hdl.handle.net/2003/34966 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-17014 | - |
dc.description.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. | en |
dc.language.iso | en | de |
dc.relation.ispartofseries | Discussion Paper / SFB823;24, 2016 | en |
dc.subject | error-in-variables models | en |
dc.subject | D-optimality | en |
dc.subject | Bayesian optimal designs | en |
dc.subject | classical errors | en |
dc.subject.ddc | 310 | - |
dc.subject.ddc | 330 | - |
dc.subject.ddc | 620 | - |
dc.title | Bayesian D-optimal designs for error-in-variables models | en |
dc.type | Text | de |
dc.type.publicationtype | workingPaper | de |
dcterms.accessRights | open access | - |
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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