Optimal two-point designs for the michaelis-menten model with heteroscedastic errors |
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Authors: | Dale Song Weng Kee Wong |
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Institution: | Department of Biostatistics , University of California , Los Angeles, CA, 90095 |
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Abstract: | We construct D-optimal designs for the Michaelis-Menten model when the variance of the response depends on the independent variable. However, this dependence is only partially known. A Bayesian approacn is used to find an optimal design by incorporating the prior lnformation about the variance structure. We demonstrate the method for a class of error variance structures and present efficiencies of these optimal designs under prior mis-specifications. In particular, we show that an erroneous assumption on the variance structure for the Michaelis-Menten model can have serious consequences. |
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Keywords: | Bayesian D-optimal designs continuous design heteroscedasticity receptor assays |
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