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dc.contributor.authorYang, Min, 1970 Oct. 28-eng
dc.contributor.authorStufken, Johneng
dc.contributor.otherUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Statisticseng
dc.descriptionDOI: 10.1214/07-AOS560eng
dc.description.abstractWe propose a new approach for identifying the support points of a locally optimal design when the model is a nonlinear model. In contrast to the commonly used geometric approach, we use an approach based on algebraic tools. Considerations are restricted to models with two parameters, and the general results are applied to often used special cases, including logistic, probit, double exponential and double reciprocal models for binary data, a loglinear Poisson regression model for count data, and the Michaelis-Menten model. The approach, which is also of value for multi-stage experiments, works both with constrained and unconstrained design regions and is relatively easy to implement.eng
dc.description.sponsorshipMin Yang was supported in part by NSF Grants DMS-07-07013 and DMS-07-48409. John Stufken was supported in part by NSF Grant DMS-07-06917.eng
dc.identifier.citationThe Annals of Statistics 2009, Vol. 37, No. 1, 518-541.eng
dc.publisherInstitute of Mathematical Statisticseng
dc.relation.ispartofStatistics publications (MU)eng
dc.subjectbinary responseeng
dc.subjectgeneralized linear modeleng
dc.subjectLoewner ordereng
dc.subject.lcshOptimal designs (Statistics)eng
dc.subject.lcshExperimental designeng
dc.titleSupport Points of Locally Optimal Designs for Nonlinear Models with Two Parameterseng

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