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http://dx.doi.org/10.5351/KJAS.2014.27.7.1269

The Maximin Robust Design for the Uncertainty of Parameters of Michaelis-Menten Model  

Kim, Youngil (School of Business and Economics, Chung-Ang University)
Jang, Dae-Heung (Department of Statistics, Pukyong National University)
Yi, Seongbaek (Department of Statistics, Pukyong National University)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.7, 2014 , pp. 1269-1278 More about this Journal
Abstract
Despite the D-optimality criterion becomes very popular in designing an experiment for nonlinear models because of theoretical foundations it provides, it is very critical that the criterion depends on the unknown parameters of the nonlinear model. But some nonlinear models turned out to be partially nonlinear in sense that the optimal design depends on the subset of parameters only. It was a strong belief that the maximin approach to find a robust design to protect against the uncertainty of parameters is not guaranteed to be successful in nonlinear models. But the maximin approach could be a success for the partial nonlinear model, because often the optimal design depends on only one unknown value of parameter, easier to handle than the full parameters. We deal with maximin approach for Michaelis-Menten model with respect to D- and $D_s$-optimality.
Keywords
D-optimality; $D_s$-optimality; maximin approach; partially nonlinear model;
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