用于相关大规模多重检验的贝叶斯隐马尔可夫模型
Bayesian Hidden Markov Models for Dependent Large-Scale Multiple Testing.
作者信息
Wang Xia, Shojaie Ali, Zou Jian
机构信息
Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio 45221, U.S.A.
Department of Biostatistics, University of Washington, Seattle, Washington 98195, U.S.A.
出版信息
Comput Stat Data Anal. 2019 Aug;136:123-136. doi: 10.1016/j.csda.2019.01.009. Epub 2019 Jan 29.
An optimal and flexible multiple hypotheses testing procedure is constructed for dependent data based on Bayesian techniques, aiming at handling two challenges, namely dependence structure and non-null distribution specification. Ignoring dependence among hypotheses tests may lead to loss of efficiency and bias in decision. Misspecification in the non-null distribution, on the other hand, can result in both false positive and false negative errors. Hidden Markov models are used to accommodate the dependence structure among the tests. Dirichlet mixture process prior is applied on the non-null distribution to overcome the potential pitfalls in distribution misspecification. The testing algorithm based on Bayesian techniques optimizes the false negative rate (FNR) while controlling the false discovery rate (FDR). The procedure is applied to pointwise and clusterwise analysis. Its performance is compared with existing approaches using both simulated and real data examples.
基于贝叶斯技术构建了一种针对相关数据的最优且灵活的多重假设检验程序，旨在应对两个挑战，即依赖结构和非零分布规范。忽略假设检验之间的依赖性可能会导致决策效率损失和偏差。另一方面，非零分布的错误设定可能会导致假阳性和假阴性错误。使用隐马尔可夫模型来适应检验之间的依赖结构。狄利克雷混合过程先验应用于非零分布，以克服分布错误设定中的潜在陷阱。基于贝叶斯技术的检验算法在控制错误发现率（FDR）的同时优化了假阴性率（FNR）。该程序应用于逐点分析和聚类分析。使用模拟和实际数据示例将其性能与现有方法进行了比较。
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