|Title:||Locally optimal designs for errors-in-variables models|
|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.|
|Subject Headings:||error-in-variable model|
|Appears in Collections:||Sonderforschungsbereich (SFB) 823|
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|DP_3114_SFB823_Konstantinou_Dette.pdf||DNB||355.85 kB||Adobe PDF||View/Open|
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