A Stepwise Cauchy Combination Test for Multiple Testing Problems with Financial Applications
提出逐步柯西组合检验(StepC)，用于处理依赖检验统计量和稀疏信号的多重检验问题，能定位驱动拒绝的p值并控制族系错误率，在金融数据中比传统方法更有效。
Abstract We propose the stepwise Cauchy combination test (StepC), a new procedure for multiple testing with dependent test statistics and sparse signals. Unlike the global version, StepC pinpoints which p-values drive rejections, while maintaining strong familywise error control. It is less conservative under dependence and more powerful than conventional multiple testing corrections. In simulations and in applications to drift burst detection and testing for nonzero alphas, StepC consistently boosts power and yields more meaningful rejections, making it a practical alternative for large-scale financial datasets.