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dc.creatorGarcía-Ródenas, R. (Ricardo)-
dc.creatorGarcía-García, J.C. (José Carlos)-
dc.creatorLópez-Fidalgo, J. (Jesús)-
dc.creatorMartín-Baos, J.Á. (José Ángel)-
dc.creatorKee Wong, W. (Weng)-
dc.date.accessioned2023-12-01T15:46:27Z-
dc.date.available2023-12-01T15:46:27Z-
dc.date.issued2020-
dc.identifier.citationGarcía-Ródenas, R. (Ricardo); García-García, J.C. (José Carlos); López-Fidalgo, J. (Jesús); et al. "A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs". Computational Statistics & Data Analysis. 144, 2020, 106844es
dc.identifier.issn1872-7352-
dc.identifier.urihttps://hdl.handle.net/10171/67987-
dc.description.abstractSeveral common general purpose optimization algorithms are compared for finding A- and D-optimal designs for different types of statistical models of varying complexity, including high dimensional models with five and more factors. The algorithms of interest include exact methods, such as the interior point method, the Nelder–Mead method, the active set method, the sequential quadratic programming, and metaheuristic algorithms, such as particle swarm optimization, simulated annealing and genetic algorithms. Several simulations are performed, which provide general recommendations on the utility and performance of each method, including hybridized versions of metaheuristic algorithms for finding optimal experimental designs. A key result is that general-purpose optimization algorithms, both exact methods and metaheuristic algorithms, perform well for finding optimal approximate experimental designs.es_ES
dc.description.sponsorshipThe research of Wong reported in this paper was partially supported by the National Institute of General Medical Sciences of the National Institutes of Health, USA under the Grant Award Number R01GM107639. López-Fidalgo and Wong were sponsored by Spanish Research Agency and fondos FEDER, Spain MTM2016-80539-C2-R1. Wong wishes to acknowledge the support from the University of Castilla–La Mancha, Spain jointly with the program FEDER of Castilla–La Mancha 2007–2013 that made his visit possible and he thanks the department for the warm hospitality during the visit. The research of García-Ródenas and Martín-Baos was supported by Ministerio de Economía, Industria y Competitividad — FEDER EU, Spain grant with number TRA2016-76914-C3-2-P. The research of García-García was supported by the predoctoral FPU fellowship from the Ministerio de Educación, Cultura y Deportes, Spain with number 16/00792.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/MECD/Formación de profesorado universitario- FPU 2016/FPU16%2F00792/ES/FPU16%2F00792es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectApproximate designes_ES
dc.subjectEfficiencyes_ES
dc.subjectEquivalence theoremes_ES
dc.subjectInformation matrixes_ES
dc.subjectMetaheuristicses_ES
dc.subjectOptimality criteriaes_ES
dc.titleA comparison of general-purpose optimization algorithms for finding optimal approximate experimental designses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.1016/j.csda.2019.106844-
dadun.citation.publicationNameComputational Statistics & Data Analysises_ES
dadun.citation.startingPage106844es_ES
dadun.citation.volume144es_ES

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