Autor(en): | Konstantinou, Maria Dette, Holger |
Titel: | Bayesian D-optimal designs for error-in-variables models |
Sprache (ISO): | en |
Zusammenfassung: | 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. |
Schlagwörter: | 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 |
Erscheinungsdatum: | 2016 |
Enthalten in den Sammlungen: | Sonderforschungsbereich (SFB) 823 |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
---|---|---|---|---|
DP_2416_SFB823_Konstantinou_Dette.pdf | DNB | 310.72 kB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource ist urheberrechtlich geschützt. |
Diese Ressource ist urheberrechtlich geschützt.
rightsstatements.org