Full Bayesian Significance Test Applied to Multivariate Normal Structure Models

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Brazilian Journal of Probability and Statistics 17:147-168 (2003)
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Abstract

Abstract: The Pull Bayesian Significance Test (FBST) for precise hy- potheses is applied to a Multivariate Normal Structure (MNS) model. In the FBST we compute the evidence against the precise hypothesis. This evi- dence is the probability of the Highest Relative Surprise Set (HRSS) tangent to the sub-manifold (of the parameter space) that defines the null hypothesis. The MNS model we present appears when testing equivalence conditions for genetic expression measurements, using micro-array technology.

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Julio Michael Stern
University of São Paulo

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