Publication: Objective Bayesian model selection approach to the two way analysis of variance
Authors
Cano, J. A. ; Carazo, C. ; Salmerón MartÃnez, Diego
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Publisher
Springer
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DOI
https://doi.org/10.1007/s00180-017-0727-1
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info:eu-repo/semantics/article
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
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.
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Citation
Computational Statistics, 2018, vol. 33, pp. 235–248
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Este Ãtem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by-nc-nd/4.0/