论文标题
针对依赖的多重测试设置的渐近最佳测试
Asymptotically optimal test for dependent multiple testing set up
论文作者
论文摘要
在本文中，我们探讨了依赖测试统计数据用于测试多个假设的行为。为了保持简单性，我们考虑了建立具有相关相关性的混合物正常模型。通过简单的线性转换，测试统计量分解为独立的组件，当有适当生成的自变量时，该组件。这些用于构建条件测试，该测试在渐近上是最佳的，其功率与使用N.P. -lemma获得的功率一样大。我们进行了广泛的模拟来支持该主张。
In this paper we explore the behaviour of dependent test statistics for testing of multiple hypothesis . To keep simplicity, we have considered a mixture normal model with equicorrelated correlation set up. With a simple linear transformation,the test statistics were decomposed into independent components, which, when conditioned appropriately generated independent variables. These were used to construct conditional tests , which were shown to be asymptotically optimal with power as large as that obtained using N.P.Lemma. We have pursued extensive simulation to support the claim.
