TY - JOUR
T1 - Optimal two-point designs for the Michaelis-Menten model with heteroscedastic errors
AU - Song, Dale
AU - Wong, Weng Kee
N1 - Funding Information:
The research of Weng Kee Wong is partially supported by a NIH research grant R29 AR44177-01A1.
PY - 1998
Y1 - 1998
N2 - 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 approach is used to find an optimal design by incorporating the prior information about the variance structure. We demonstrate the method for a class of error variance structures and present efficiencies of these optimal designs under prior misspecifications. In particular, we show that an erroneous assumption on the variance structure for the Michaelis-Menten model can have serious consequences.
AB - 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 approach is used to find an optimal design by incorporating the prior information about the variance structure. We demonstrate the method for a class of error variance structures and present efficiencies of these optimal designs under prior misspecifications. 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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U2 - 10.1080/03610929808832173
DO - 10.1080/03610929808832173
M3 - Article
AN - SCOPUS:0039825892
SN - 0361-0926
VL - 27
SP - 1503
EP - 1516
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 6
ER -