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Journal of Computer Science & Systems Biology | ISSN: 0974-7230 | Volume: 11
&
Biostatistics and Bioinformatics
Big Data Analytics & Data Mining
7
th
International Conference on
7
th
International Conference on
September 26-27, 2018 | Chicago, USA
Bayesian multiple testing under sparsity
Malay Ghosh
University of Florida, USA
T
his talk reviews certain Bayesian procedures that have recently been proposed to address multiple testing under sparsity. Consider
the problem of simultaneous testing for the means of independent normal observations. In this talk, we study asymptotic
optimality properties of certain multiple testing rules in a Bayesian decision theoretic framework, where the overall loss of a multiple
testing rule is taken as the number of misclassified hypotheses. The multiple testing rules that are considered, include spike and
slab priors as well as a general class of one-group shrinkage priors for the mean parameters. The latter is rich enough to include,
among others, the families of three parameter beta, generalized double Pareto priors and in particular the horseshoe, the normal-
exponential-gamma and the Strawderman-Berger priors. Within the chosen asymptotic framework, the multiple testing rules under
study asymptotically attain the risk of the Bayes Oracle. Some classical multiple testing procedures are also evaluated within the
proposed Bayesian framework.
ghoshm@ufl.eduJ Comput Sci Syst Biol 2018, Volume: 11
DOI: 10.4172/0974-7230-C1-021