Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/14963
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.
ISSN: 1524-1904
DOI: 10.1002/asmb.2226
Rights: © Wiley
Type: Article
Affiliation : Ruhr-Universität Bochum 
Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

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