Support points of locally optimal designs for nonlinear models with two parameters

dc.creatorYang, Min
dc.creatorStufken, John
dc.date2009-03-04
dc.date.accessioned2026-07-07T12:49:01Z
dc.date.available2026-07-07T12:49:01Z
dc.descriptionWe 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.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-AOS560 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0903.0728
dc.identifierhttp://arxiv.org/abs/0903.0728
dc.identifierAnnals of Statistics 2009, Vol. 37, No. 1, 518-541
dc.identifierdoi:10.1214/07-AOS560
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/222259
dc.subjectMethodology
dc.subjectStatistics Theory
dc.subject62K05 (Primary) 62J12 (Secondary)
dc.titleSupport points of locally optimal designs for nonlinear models with two parameters
dc.typetext

Files

Collections