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dc.contributor.authorYang, Min, 1970 Oct. 28-
dc.contributor.authorStufken, John
dc.contributor.otherUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Statistics
dc.date.issued2009
dc.descriptionDOI: 10.1214/07-AOS560en_US
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.en_US
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.en_US
dc.identifier.citationThe Annals of Statistics 2009, Vol. 37, No. 1, 518-541.en_US
dc.identifier.urihttp://hdl.handle.net/10355/9127
dc.publisherInstitute of Mathematical Statisticsen_US
dc.relation.ispartofStatistics publications (MU)
dc.subjectbinary responseen_US
dc.subjectgeneralized linear modelen_US
dc.subjectLoewner orderen_US
dc.subject.lcshOptimal designs (Statistics)
dc.subject.lcshExperimental design
dc.titleSupport Points of Locally Optimal Designs for Nonlinear Models with Two Parametersen_US
dc.typeArticleen_US


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