Locally optimal designs for errors-in-variables models

M. Konstantinou and H. Dette

Biometrika, 2015, vol. 102, issue 4, 951-958

Abstract: 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, $E_{\rm max}$ 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.

Date: 2015
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Citations: View citations in EconPapers (2)

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