D-optimal designs based on the second-order least squares estimator

dc.contributor.authorGao, Lucy L.
dc.contributor.authorZhou, Julie
dc.date.accessioned2016-06-28T20:51:09Z
dc.date.available2016-06-28T20:51:09Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.description.abstractWhen the error distribution in a regression model is asymmetric, the second-order least squares estimator (SLSE) is more efficient than the ordinary least squares estimator. This result motivated the research in Gao and Zhou (J Stat Plan Inference 149:140–151, 2014), where A-optimal and D-optimal design criteria based on the SLSE were proposed and various design properties were studied. In this paper, we continue to investigate the optimal designs based on the SLSE and derive new results for the D-optimal designs. Using convex optimization techniques and moment theories, we can construct D-optimal designs for univariate polynomial and trigonometric regression models on any closed interval. Several theoretical results are obtained. The methodology is quite general. It can be applied to reduced polynomial models, reduced trigonometric models, and other regression models. It can also be extended to A-optimal designs based on the SLSE.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. The authors thank Professor Jiawang Nie for his valuable suggestions that lead to the development of Algorithm I. The authors are also grateful to the Editor and reviewers for their helpful comments and suggestions.en_US
dc.identifier.citationGao, L.L., & Zhou, J. (2015). D-optimal designs based on the second-order least squares estimator. Statistical Papers, 1-18.en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s00362-015-0688-9
dc.identifier.urihttp://hdl.handle.net/1828/7383
dc.language.isoenen_US
dc.publisherStatistical Papersen_US
dc.subjectAsymmetric distribution
dc.subjectconvex optimization
dc.subjectmoment theory
dc.subjectoptimal design
dc.subjectpolynomial regression
dc.subjecttrigonometric regression
dc.subject.departmentDepartment of Mathematics and Statistics
dc.titleD-optimal designs based on the second-order least squares estimatoren_US
dc.typePostprinten_US

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Gao_Lucy_StatPap_2015.pdf
Size:
313.76 KB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.74 KB
Format:
Item-specific license agreed upon to submission
Description: