Publication: Unscaled Bayes factors for multiple hypothesis testing in microarray experiments
dc.affiliation.dpto | UC3M. Departamento de EstadĂstica | es |
dc.contributor.author | Bertolino, Francesco | |
dc.contributor.author | Cabras, Stefano | |
dc.contributor.author | Castellanos, MarĂa Eugenia | |
dc.contributor.author | Racugno, Walter | |
dc.contributor.funder | Comunidad de Madrid | es |
dc.date.accessioned | 2021-07-07T08:59:29Z | |
dc.date.available | 2021-07-07T08:59:29Z | |
dc.date.issued | 2015-12-01 | |
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. | en |
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. | en |
dc.format.extent | 14 | |
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. | en |
dc.identifier.doi | https://doi.org/10.1177%2F0962280212437827 | |
dc.identifier.issn | 0962-2802 | |
dc.identifier.publicationfirstpage | 1030 | |
dc.identifier.publicationissue | 6 | |
dc.identifier.publicationlastpage | 1043 | |
dc.identifier.publicationtitle | Statistical Methods in Medical Research | en |
dc.identifier.publicationvolume | 24 | |
dc.identifier.uri | https://hdl.handle.net/10016/33012 | |
dc.identifier.uxxi | AR/0000017830 | |
dc.language.iso | eng | |
dc.publisher | SAGE | |
dc.relation.projectID | Comunidad de Madrid. S2009/ESP-1594 | es |
dc.rights | © The Authors, 2015. | en |
dc.rights.accessRights | open access | en |
dc.subject.eciencia | EstadĂstica | es |
dc.subject.eciencia | InformĂ¡tica | es |
dc.subject.eciencia | MatemĂ¡ticas | es |
dc.subject.other | False discovery rate | en |
dc.subject.other | Improper priors | en |
dc.subject.other | Local false discovery rate | en |
dc.title | Unscaled Bayes factors for multiple hypothesis testing in microarray experiments | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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