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dc.contributor.authorXampeny Solani, Rafael
dc.contributor.authorGrima Cintas, Pedro
dc.contributor.authorTort Martorell, X.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.identifier.citationXampeny, R., Grima, P., Tort, X. Selecting significant effects in factorial designs: Lenth’s method versus the Box-Meyer approach. "Journal of applied statistics", 1 Gener 2018, vol. 46, núm.8, p.1404-1416
dc.description.abstractThe Lenth method is conceptually simple and probably the most common approach to analyzing the significance of the effects in factorial designs. Here, we compare it with a Bayesian approach proposed by Box and Meyer and which does not appear in the usual software packages. The comparison is made by simulating the results of 4, 8 and 16 run designs in a set of scenarios that mirror practical situations and analyzing the results provided by both methods. Although the results depend on the number of runs and the scenario considered, the use of the Box and Meyer method generally produces better results. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
dc.publisherTaylor & Francis
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.lcshMathematical statistics
dc.subject.otherBox-Meyer method
dc.subject.otherFactorial designfour-run experiments
dc.subject.otherLenth method
dc.subject.othersignificant effects
dc.titleSelecting significant effects in factorial designs: Lenth’s method versus the Box-Meyer approach
dc.subject.lemacEstadística matemàtica
dc.subject.lemacEstadística -- Metodologia
dc.contributor.groupUniversitat Politècnica de Catalunya. ADBD - Anàlisi de Dades Complexes per a les Decisions Empresarials
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorXampeny, R., Grima, P., Tort, X.
upcommons.citation.publicationNameJournal of applied statistics

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