Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/14964
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. | URI: | https://hdl.handle.net/20.500.14279/14964 | ISSN: | 00063444 | DOI: | 10.1093/biomet/asv048 | Rights: | © Biometrika Trust | Type: | Article | Affiliation : | Ruhr-Universität Bochum | Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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