Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/14964
Title: Locally optimal designs for errors-in-variables models
Authors: Konstantinou, Maria 
Dette, H. 
Major Field of Science: Agricultural Sciences
Field Category: Environmental Biotechnology;Other Agricultural Sciences
Keywords: Classical error;D-optimality;Measurement error model;Optimal design
Issue Date: 23-Oct-2015
Source: Biometrika, 2015, vol. 102, no. 4, pp. 951-958.
Volume: 102
Issue: 4
Start page: 951
End page: 958
Journal: Biometrika 
Abstract: © 2015 Biometrika Trust. We consider the construction of optimal designs for nonlinear regression models when there are measurement errors in the covariates. Corresponding approximate design theory is developed for maximum likelihood and least-squares estimation, with the latter leading to nonconcave optimization problems. Analytical characterizations of the locally D-optimal saturated designs are provided for the Michaelis-Menten, Emax and exponential regression models. Through concrete applications, we illustrate how measurement errors in the covariates affect the optimal choice of design and show that the locally D-optimal saturated designs are highly efficient for relatively small misspecifications of the parameter values.
ISSN: 0006-3444
DOI: 10.1093/biomet/asv048
Rights: © Biometrika Trust
Attribution-NonCommercial-NoDerivs 3.0 United States
Type: Article
Affiliation : Ruhr-Universität Bochum 
Appears in Collections:Άρθρα/Articles

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