The Optimal Discovery Procedure and Bayesian Decision Rules Peter Mueller Department of Biostatistics University of Texas M.D. Anderson Cancer Center Abstract
We discuss an
interpretation of the optimal discovery procedure (ODP, Storey 2006) as
an approximate Bayes rule in a nonparametric Bayesian model for
multiple comparisons. An improved approximation defines a
non-parametric Bayesian version of the ODP statistic (BODP). The
definition includes multiple shrinkage in clusters. In a simulation
study and a data analysis example we show a (small) improvement in frequentist
summaries. The BODP allows easy modifications for dependence of the
comparisons and other extensions of the ODP.
Joint work with Michele Guindani and Song Zhang |