论文标题
改进了用于离散统一和均匀测试的$ Q $价值：比较研究
Improved $q$-values for discrete uniform and homogeneous tests: a comparative study
论文作者
论文摘要
大规模离散统一和均匀的$ p $值通常在多次测试的应用中出现。例如，每当在整个基因基因座中应用非参数（或两样本）测试时，这就会发生在基因组广泛的关联研究中。在本文中，我们根据几个现有的null假设比例（$π_0$）考虑了此类情况的$ q $价值，这些估计值$π_0$，这些估计考虑了$ p $ - 价值的离散性。审查了几种方法的理论保证，并审查了$π_0$和错误发现率控制的理论保证。通过密集的蒙特卡洛模拟研究了离散$ Q $价值的性能，包括位置，规模和综合测试，以及可能依赖的$ p $ - 价值。这些方法也适用于遗传和财务数据，以进行说明目的。由于用于计算$ Q $值的$π_0$的特定估计器可能会影响审查程序的功率，相对优势和缺点。给出了实用的建议。
Large scale discrete uniform and homogeneous $P$-values often arise in applications with multiple testing. For example, this occurs in genome wide association studies whenever a nonparametric one-sample (or two-sample) test is applied throughout the gene loci. In this paper we consider $q$-values for such scenarios based on several existing estimators for the proportion of true null hypothesis, $π_0$, which take the discreteness of the $P$-values into account. The theoretical guarantees of the several approaches with respect to the estimation of $π_0$ and the false discovery rate control are reviewed. The performance of the discrete $q$-values is investigated through intensive Monte Carlo simulations, including location, scale and omnibus nonparametric tests, and possibly dependent $P$-values. The methods are applied to genetic and financial data for illustration purposes too. Since the particular estimator of $π_0$ used to compute the $q$-values may influence the power, relative advantages and disadvantages of the reviewed procedures are discussed. Practical recommendations are given.
