Abstract
Multiple hypothesis tests is a topic which has recently shown a major expansion, mainly due to the expansion of the methodology developed in connection with genomics. These new methods allow scientists to handle simultaneously thousands of null hypotheses. The frequentist approach to this problem consists of using different error measures in testing so that to ensure the Type I error remains below a desired level. This paper introduces a parametric Bayesian analysis to determine the hypotheses to be considered as being significant (i.e., useful) for a posterior deeper analysis. The results are to be compared with the frequentist methodology of the false discovery rate (FDR). Differences between both approaches are shown by means of simulation examples.
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Research supported from MEC, UCM, CM-UCM.
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Gómez-Villegas, M.A., González-Pérez, B. (2018). Multiple Hypothesis Tests: A Bayesian Approach. In: Gil, E., Gil, E., Gil, J., Gil, M. (eds) The Mathematics of the Uncertain. Studies in Systems, Decision and Control, vol 142. Springer, Cham. https://doi.org/10.1007/978-3-319-73848-2_19
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DOI: https://doi.org/10.1007/978-3-319-73848-2_19
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