Unscaled Bayes factors for multiple hypothesis testing in microarray experiments

e-Archivo Repository

Show simple item record

dc.contributor.author Bertolino, Francesco
dc.contributor.author Cabras, Stefano
dc.contributor.author Castellanos, María Eugenia
dc.contributor.author Racugno, Walter
dc.date.accessioned 2021-07-07T08:59:29Z
dc.date.available 2021-07-07T08:59:29Z
dc.date.issued 2015-12-01
dc.identifier.bibliographicCitation Bertolino, F., Cabras, S., Castellanos, M. E. & Racugno, W. (2015). Unscaled Bayes factors for multiple hypothesis testing in microarray experiments. Statistical Methods in Medical Research, 24(6), pp. 1030-1043.
dc.identifier.issn 0962-2802
dc.identifier.uri http://hdl.handle.net/10016/33012
dc.description.abstract Multiple hypothesis testing collects a series of techniques usually based on p-values as a summary of the available evidence from many statistical tests. In hypothesis testing, under a Bayesian perspective, the evidence for a specified hypothesis against an alternative, conditionally on data, is given by the Bayes factor. In this study, we approach multiple hypothesis testing based on both Bayes factors and p-values, regarding multiple hypothesis testing as a multiple model selection problem. To obtain the Bayes factors we assume default priors that are typically improper. In this case, the Bayes factor is usually undetermined due to the ratio of prior pseudo-constants. We show that ignoring prior pseudo-constants leads to unscaled Bayes factor which do not invalidate the inferential procedure in multiple hypothesis testing, because they are used within a comparative scheme. In fact, using partial information from the p-values, we are able to approximate the sampling null distribution of the unscaled Bayes factor and use it within Efron's multiple testing procedure. The simulation study suggests that under normal sampling model and even with small sample sizes, our approach provides false positive and false negative proportions that are less than other common multiple hypothesis testing approaches based only on p-values. The proposed procedure is illustrated in two simulation studies, and the advantages of its use are showed in the analysis of two microarray experiments.
dc.description.sponsorship Authors F. Bertolino, S. Cabras and W. Racugno were partially supported by the Italian Ministry of Education, University and Research. M.E. Castellanos was partially supported by the Spanish Ministry of Science and Technology, under grant MTM2010-19528, CAM of Spain under grant S2009/esp-1594 and the visiting professor program of Regione Autonoma della Sardegna of Italy.
dc.format.extent 14
dc.language.iso eng
dc.publisher SAGE
dc.rights © The Authors, 2015.
dc.subject.other False discovery rate
dc.subject.other Improper priors
dc.subject.other Local false discovery rate
dc.title Unscaled Bayes factors for multiple hypothesis testing in microarray experiments
dc.type article
dc.subject.eciencia Estadística
dc.subject.eciencia Informática
dc.subject.eciencia Matemáticas
dc.identifier.doi https://doi.org/10.1177%2F0962280212437827
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2009/ESP-1594
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 1030
dc.identifier.publicationissue 6
dc.identifier.publicationlastpage 1043
dc.identifier.publicationtitle Statistical Methods in Medical Research
dc.identifier.publicationvolume 24
dc.identifier.uxxi AR/0000017830
dc.contributor.funder Comunidad de Madrid
dc.affiliation.dpto UC3M. Departamento de Estadística
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)

This item appears in the following Collection(s)

Show simple item record