Designing Experiments for Nonlinear Models - An Introduction
Johnson, Rachel T.
Montgomery, Douglas C.
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We illustrate the construction of Bayesian D-optimal designs for nonlinear models and compare the relative efficiency of standard designs with these designs for several models and prior distributions on the parameters. Through a relative efficiency analysis, we show that standard designs can perform well in situations where the nonlinear model is intrinsically linear. However, if the model is nonlinear and its expectation function cannot be linearized by simple transformations, the nonlinear optimal design is considerably more efficient than the standard design.
The article of record as published may be found at http://dx.doi.org/10.1002/qre.1063
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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