基于FCR控制的选择性符号确定多置信区间
Selective Sign-Determining Multiple Confidence Intervals with FCR Control
摘要：
给定$m$未知参数和相应的独立估计器，Benjamini-Hochberg（BH）过程可以用于对参数的符号进行分类，使得错误方向决策（方向FDR）的预期比例被控制在预设水平$q$。更雄心勃勃的是，我们的目标是构建符号确定置信区间--而不是只分类的符号--这样的预期比例的非覆盖构造区间（FCR）的控制。我们建议一个有效的程序，调整边际置信区间，以构建一个最大数量的符号确定置信区间。我们提出了一个新的边际置信区间，专门为我们的程序，它允许平衡的权力和长度之间的权衡构建的间隔，事实上，往往享受（几乎）两全其美。我们应用我们的方法来检测一项高度公开的社会神经科学研究中相关性的迹象，并在第二个例子中，检测GWAS数据中SNP与2型糖尿病的关联方向。在这两个例子中，我们比较我们的程序与现有的方法，并获得令人鼓舞的结果。
英文摘要：
Given $m$ unknown parameters with corresponding independent estimators, the Benjamini-Hochberg (BH) procedure can be used to classify the sign of parameters such that the expected proportion of erroneous directional decisions (directional FDR) is controlled at a preset level $q$. More ambitiously, our goal is to construct sign-determining confidence intervals---instead of only classifying the sign---such that the expected proportion of non-covering constructed intervals (FCR) is controlled. We suggest a valid procedure which adjusts a marginal confidence interval in order to construct a maximum number of sign-determining confidence intervals. We propose a new marginal confidence interval, designed specifically for our procedure, which allows to balance a trade-off between power and length of the constructed intervals, and, in fact, often enjoy (almost) the best of both worlds. We apply our methods to detect the sign of correlations in a highly publicized social neuroscience study and, in a second example, to detect the direction of association for SNPs with Type-2 Diabetes in GWAS data. In both examples we compare our procedure to existing methods and obtain encouraging results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
