文章基本信息

标题：Full Bayesian Significance Test Applied to Multivariate Normal Structure Models
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作者：M. LAURETTO ; C. A. B. PEREIRA ; J. M. STERN 等
期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2003
卷号：17
期号：2
页码：147-168
出版社：Brazilian Statistical Association
摘要：The Full Bayesian Significance Test (FBST) for precise hy-potheses is applied to a Multivariate Normal Structure (MNS) model. Inthe FBST we compute the evidence against the precise hypothesis. This evi-dence is the probability of the Highest Relative Surprise Set (HRSS) tangentto the sub-manifold (of the parameter space) that defines the null hypothesis.The MNS mo del we present appears when testing equivalence conditions forgenetic expression measurements, using micro-array technology
关键词：Credibility; evidence; full Bayesian significance test; relative;surprise; structural mo dels for multivariate normals