A Conservative Test for Multiple Comparison Based on Highly Correlated Test Statistics
A Conservative Test for Multiple Comparison Based on Highly Correlated Test Statistics
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DOI：
10.1111/j.1541-0420.2007.00821.x
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发表时间：
2007-12
期刊：
影响因子：
1.9
通讯作者：
Yoshiyuki Ninomiya;H. Fujisawa
中科院分区：
文献类型：
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作者：
Yoshiyuki Ninomiya;H. Fujisawa
摘要在遗传学中，我们经常会遇到大量高度相关的测试统计量。多重比较最著名的保守界是Bonferroni界，它适用于检验统计量是独立的，但不适用于检验统计量高度相关的情况。本文提出了一个新的保守界，它不需要多次积分就可以很容易地计算出来，当检验统计量高度相关时，它是一个很好的近似。通过仿真和实际数据分析对该方法的性能进行了评估。
Summary In genetics, we often encounter a large number of highly correlated test statistics. The most famous conservative bound for multiple comparison is Bonferroni's bound, which is suitable when the test statistics are independent but not when the test statistics are highly correlated. This article proposes a new conservative bound that is easily calculated without multiple integration and is a good approximation when the test statistics are highly correlated. The performance of the proposed method is evaluated by simulation and real data analysis.