Multiple Testing of General Contrasts Using Logical Constraints and Correlations
提出一种算法，利用假设间的逻辑约束和检验统计量间的相关性来提升逐步检验的统计功效，并通过调整p值汇总结果，使用广义最小二乘混合蒙特卡洛方法高效计算。
Abstract Use of logical constraints among hypotheses and correlations among test statistics can greatly improve the power of step-down tests. An algorithm for uncovering these logically constrained subsets in a given dataset is described. The multiple testing results are summarized using adjusted p values, which incorporate the relevant dependence structures and logical constraints. These adjusted p values are computed consistently and efficiently using a generalized least squares hybrid of simple and control-variate Monte Carlo methods, and the results are compared to alternative stepwise testing procedures. Key Words: Adjusted p valueControl variateLinear modelMonte CarloMultiple comparisonsSimultaneous inference