首页> 外文期刊>Bernoulli: official journal of the Bernoulli Society for Mathematical Statistics and Probability >Tail approximations for the Student t-, F-, and Welch statistics for non-normal and not necessarily i.i.d. random variables
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Tail approximations for the Student t-, F-, and Welch statistics for non-normal and not necessarily i.i.d. random variables

机译:非正常且不一定是i.d.的学生t,F和Welch统计量的尾部近似值。随机变量

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摘要

Let T be the Student one- or two-sample t-, F-, or Welch statistic. Now release the underlying assumptions of normality, independence and identical distribution and consider a more general case where one only assumes that the vector of data has a continuous joint density. We determine asymptotic expressions for P(T > u) as u -> infinity for this case. The approximations are particularly accurate for small sample sizes and may be used, for example, in the analysis of High-Throughput Screening experiments, where the number of replicates can be as low as two to five and often extreme significance levels are used. We give numerous examples and complement our results by an investigation of the convergence speed - both theoretically, by deriving exact bounds for absolute and relative errors, and by means of a simulation study.
机译:令T为学生一样本或二样本t,F或Welch统计量。现在释放正态性,独立性和相同分布的基本假设,并考虑一种更一般的情况,其中仅假设数据向量具有连续的联合密度。在这种情况下,我们将P(T> u)的渐近表达式确定为u->无穷大。该近似值对于小样本量特别准确,并且可以用于例如高通量筛选实验的分析,在该实验中,重复次数可以低至2到5,并且通常使用极高的显着性水平。我们提供了许多示例,并通过研究收敛速度来补充我们的结果-理论上是通过得出绝对误差和相对误差的精确范围以及通过仿真研究得出的。

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