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dc.contributor.authorJohnson, Rachel T.
dc.contributor.authorMontgomery, Douglas C.
dc.date.accessioned2014-12-12T22:38:12Z
dc.date.available2014-12-12T22:38:12Z
dc.date.issued2009-08
dc.identifier.citationQuality and Reliability Engineering International, Volume 26, pp. 431-441, 2009
dc.identifier.urihttp://hdl.handle.net/10945/44163
dc.descriptionThe article of record as published may be found at http://dx.doi.org/10.1002/qre.1063en_US
dc.description.abstractWe 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.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleDesigning Experiments for Nonlinear Models - An Introductionen_US
dc.typeArticleen_US
dc.subject.authoroptimal designen_US
dc.subject.authorfactorial designen_US
dc.subject.authorBayesian D-optimalen_US


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