Abstract Many estimators for the proportion of true null hypotheses in the literature, which are defined for independent p -values, struggle under dependence. In particular, the variance of the classical Schweder–Spjotvoll estimator increases with the degree of dependence among the p -values. We propose a technique based on the independent-component bootstrap, which considerably improves this behavior. The idea of our marginal bootstrap modification is to utilize the conditional independence of the bootstrapped p -values and bagging to reduce the variance of the estimator. The theoretical validity of the resulting Bootstrap-Schweder–Spjotvoll procedure is analyzed and its performance is illustrated on simulated data.

中文翻译：

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估计依赖项下真实零假设的比例：边际引导方法

摘要 文献中为独立 p 值定义的真实零假设比例的许多估计量在依赖下挣扎。特别是，经典 Schweder-Spjotvoll 估计量的方差随着 p 值之间的依赖程度而增加。我们提出了一种基于独立组件引导程序的技术，它大大改善了这种行为。我们的边际自举修改的想法是利用自举 p 值和装袋的条件独立性来减少估计量的方差。分析了由此产生的 Bootstrap-Schweder-Spjotvoll 程序的理论有效性，并在模拟数据上说明了其性能。