RT Journal Article
T1 Bayesian Analysis of Multiple Hypothesis Testing with Applications to Microarray Experiments
A1 Ausin, A. C.
A1 Gómez Villegas, Miguel Ángel
A1 González Pérez, Beatriz
A1 Rodríguez Bernal, María Teresa
A1 Salazar Mendoza, Isabel
A1 Sanz San Miguel, Luis
AB Recently, the field of multiple hypothesis testing has experienced a great expansion, basically because of the new methods developed in the field of genomics. These new methods allow scientists to simultaneously process thousands of hypothesis tests. The frequentist approach to this problem is made by using different testing error measures that allow to control the Type I error rate at a certain desired level. Alternatively, in this article, a Bayesian hierarchical model based on mixture distributions and an empirical Bayes approach are proposed in order to produce a list of rejected hypotheses that will be declared significant and interesting for a more detailed posterior analysis. In particular, we develop a straightforward implementation of a Gibbs sampling scheme where all the conditional posterior distributions are explicit. The results are compared with the frequentist False Discovery Rate (FDR) methodology. Simulation examples show that our model improves the FDR procedure in the sense that it diminishes the percentage of false negatives keeping an acceptable percentage of false positives.
PB Taylor & Francis
SN 0361-0926
YR 2011
FD 2011
LK https://hdl.handle.net/20.500.14352/42207
UL https://hdl.handle.net/20.500.14352/42207
LA eng
NO Ausín, M. C., Gómez Villegas, M. Á., González Pérez, B. et al. «Bayesian Analysis of Multiple Hypothesis Testing with Applications to Microarray Experiments». Communications in Statistics - Theory and Methods, vol. 40, n.o 13, abril de 2011, pp. 2276-91. DOI.org (Crossref), https://doi.org/10.1080/03610921003778183.
NO Ministerio de Educación, Formación Profesional y Deportes (España)
NO Comunidad de Madrid
NO Universidad Complutense de Madrid
DS Docta Complutense
RD 18 abr 2025