Optimal designs for regression models using the second-order least squares estimator

dc.contributor.authorYin, Yue
dc.contributor.authorZhou, Julie
dc.date.accessioned2017-07-31T18:55:05Z
dc.date.available2017-07-31T18:55:05Z
dc.date.copyright2017en_US
dc.date.issued2017-07-31
dc.descriptionThe final publication is expected to appear in Volume 27, Number 4, October 2017en_US
dc.description.abstractWe investigate properties and numerical algorithms for A- and D-optimal regression designs based on the second-order least squares estimator (SLSE). Several results are derived, including a characterization of the A-optimality criterion. We can formulate the optimal design problems under SLSE as semide nite programming or convex optimization problems and we show that the resulting algorithms can be faster than more conventional multiplicative algorithms, especially in nonlinear models. Our results also indicate that the optimal designs based on the SLSE are more e cient than those based on the ordinary least squares estimator, provided the error distribution is highly skewed.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThis research work is supported by Discovery Grants from the Natural Science and Engineering Research Council of Canada.en_US
dc.identifier.urihttp://dx.doi.org/10.5705/ss.202015.0285
dc.identifier.urihttp://hdl.handle.net/1828/8392
dc.language.isoenen_US
dc.subjectA-optimal designen_US
dc.subjectconvex optimizationen_US
dc.subjectD-optimal designen_US
dc.subjectmultiplicative algorithmen_US
dc.subjectnonlinear modelen_US
dc.subjectSeDuMien_US
dc.subjecttransformation invarianceen_US
dc.titleOptimal designs for regression models using the second-order least squares estimatoren_US
dc.typePreprinten_US

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