Locally optimal designs for errors-in-variables models
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Date
2014-09-05
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Abstract
This paper considers the construction of optimal designs for nonlinear regres-
sion models when there are measurement errors in the predictor. Corresponding
(approximate) design theory is developed for maximum likelihood and least squares
estimation, where the latter leads to non-concave optimisation problems. For the
Michaelis-Menten, EMAX and exponential regression model D-optimal designs can
be found explicitly and compared with the corresponding designs derived under the
assumption of no measurement error in concrete applications.
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Keywords
error-in-variable model, D-optimality, nonlinear regression, optimal design