Large-scale simultaneous testing with hypergeometric inverted-beta priors
Large-scale simultaneous testing with hypergeometric inverted-beta priors
复制标题
使用超几何倒β先验进行大规模同步测试
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
James G. Scott
中科院分区：
文献类型：
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作者：
Nicholas G. Polson;James G. Scott
我们开发了一类新的分发版本，用于大规模的同时测试。这些先验是基于超几何倒贝塔先验，并具有两个主要的吸引人的特征：重尾和计算可处理性。该族是正态/倒贝塔先验的四参数推广，是分层正态模型中收缩系数的自然共轭先验。我们的结果强调了这些重尾先验在大型多重测试问题中的有效性，因为它们在边际可能性m(Y)|中具有温和的尾部衰减率|这是长期以来被认为在测试中重要的性质。我们应用我们提出的方法，对93个国家和地区的11,298个公开交易的RMS进行了ROA(资产回报率)的历史模式测试。我们的目标是
We develop a new class of distributions for use in large-scale simultaneous testing. These priors are based on hypergeometric inverted-beta priors, and have two main attractive features: heavy tails, and computational tractability. The family is a four-parameter generalization of the normal/inverted-beta prior, and is the natural conjugate prior for a shrinkage coecients in a hierarchical normal model. Our results emphasize the usefulness of these of heavy-tailed priors in large multiple-testing problems, as they have mild rate of tail decay in the marginal likelihood m(y)|a property long recognized to be important in testing. We apply our proposed methodology by testing historical patterns of ROA (return on assets) for a cohort of 11,298 publicly traded rms across 93 countries. Our goal is