Objective Bayesian model selection approach to the two way analysis of variance

Cano, J. A.; Carazo, C.; Salmerón Martínez, Diego

Publication:
Objective Bayesian model selection approach to the two way analysis of variance

dc.contributor.author	Cano, J. A.
dc.contributor.author	Carazo, C.
dc.contributor.author	Salmerón Martínez, Diego
dc.contributor.department	Ciencias Sociosanitarias
dc.date.accessioned	2025-01-28T10:48:02Z
dc.date.available	2025-01-28T10:48:02Z
dc.date.issued	2018-03
dc.description	© 2017, Springer-Verlag. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Computational Statistics. To access the final edited and published work see https://doi.org/10.1007/s00180-017-0727-1	es
dc.description.abstract	An objective Bayesian procedure for testing in the two way analysis of variance is proposed. In the classical methodology the main effects of the two factors and the interaction effect are formulated as linear contrasts between means of normal populations, and hypotheses of the existence of such effects are tested. In this paper, for the first time these hypotheses have been formulated as objective Bayesian model selection problems. Our development is under homoscedasticity and heteroscedasticity, providing exact solutions in both cases. Bayes factors are the key tool to choose between the models under comparison but for the usual default prior distributions they are not well defined. To avoid this difficulty Bayes factors for intrinsic priors are proposed and they are applied in this setting to test the existence of the main effects and the interaction effect. The method has been illustrated with an example and compared with the classical method. For this example, both approaches went in the same direction although the large P value for interaction (0.79) only prevents us against to reject the null, and the posterior probability of the null (0.95) was conclusive.	es
dc.format	application/pdf	es
dc.format.extent	14	es
dc.identifier.citation	Computational Statistics, 2018, vol. 33, pp. 235–248
dc.identifier.doi	https://doi.org/10.1007/s00180-017-0727-1
dc.identifier.issn	Print: 0943-4062
dc.identifier.issn	Electronic: 1613-9658
dc.identifier.uri	http://hdl.handle.net/10201/149448
dc.language	eng	es
dc.publisher	Springer	es
dc.relation	This research was supported by the Séneca Foundation Programme for the Generation of Excellence Scientific Knowledge under Project 15220/PI/10.	es
dc.relation.publisherversion	https://link.springer.com/article/10.1007/s00180-017-0727-1	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Bayes factors	es
dc.subject	Intrinsic priors	es
dc.subject	Linear contrasts	es
dc.title	Objective Bayesian model selection approach to the two way analysis of variance	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es