Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/14963
Title: | Bayesian D-optimal designs for error-in-variables models |
Authors: | Konstantinou, Maria Dette, Holger |
Major Field of Science: | Agricultural Sciences |
Field Category: | Environmental Biotechnology;Other Agricultural Sciences |
Keywords: | Bayesian optimal designs;classical errors;D-optimality;error-in-variables models |
Issue Date: | 1-May-2017 |
Source: | Applied Stochastic Models in Business and Industry, vol. 33, no. 3, pp. 269-281 |
Volume: | 33 |
Issue: | 3 |
Start page: | 269 |
End page: | 281 |
Journal: | Applied Stochastic Models in Business and Industry |
Abstract: | Copyright © 2017 John Wiley & Sons, Ltd. Bayesian optimality criteria provide a robust design strategy to parameter misspecification. We develop an approximate design theory for Bayesian D-optimality for nonlinear 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 with several other designs, including the corresponding locally D-optimal designs, which are often used in practice. Copyright © 2017 John Wiley & Sons, Ltd. |
URI: | https://hdl.handle.net/20.500.14279/14963 |
ISSN: | 15241904 |
DOI: | 10.1002/asmb.2226 |
Rights: | © Wiley |
Type: | Article |
Affiliation : | Ruhr-Universität Bochum Cyprus University of Technology |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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