On fuzzy familywise error rate and false discovery rate procedures for discrete distributions
On fuzzy familywise error rate and false discovery rate procedures for discrete distributions
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DOI：
10.1093/biomet/asn061
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发表时间：
2009-03-01
期刊：
影响因子：
2.7
通讯作者：
Lewin, Alex
中科院分区：
文献类型：
--
作者：
Kulinskaya, Elena;Lewin, Alex
引入模糊多重比较方法，解决离散检验统计量的多重比较问题。随机p值的临界函数被提出作为反对零假设的证据的度量。随机试验的经典概念被扩展到多重比较。这种方法使为连续分布统计发展的所有多重比较理论自动适用于离散情况。讨论了家族错误率和错误发现率方法的实例，并给出了在联动不平衡检验中的应用。实施这些程序的软件是可用的。
Fuzzy multiple comparisons procedures are introduced as a solution to the problem of multiple comparisons for discrete test statistics. The critical function of the randomized p-values is proposed as a measure of evidence against the null hypotheses. The classical concept of randomized tests is extended to multiple comparisons. This approach makes all theory of multiple comparisons developed for continuously distributed statistics automatically applicable to the discrete case. Examples of familywise error rate and false discovery rate procedures are discussed and an application to linkage disequilibrium testing is given. Software for implementing the procedures is available.