Performance of nonparametric multiple comparison tests under heteroscedasticity, dependency, and skewed error distribution
Performance of nonparametric multiple comparison tests under heteroscedasticity, dependency, and skewed error distribution
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DOI：
10.1080/03610918.2016.1146761
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发表时间：
2017-01-01
影响因子：
0.9
通讯作者：
Demirhan, Haydar
中科院分区：
文献类型：
--
作者：
Dolgun, Anil;Demirhan, Haydar
在这篇文章中，进行了广泛的蒙特卡罗模拟研究，以评估和比较非参数多重比较检验违反经典的方差分析假设。Monte Carlo研究的模拟空间由288种不同的平衡和不平衡样本量、组数、治疗效应、各种水平的方差异质性、亚组水平之间的依赖性和单因素实验设计下的偏态误差分布组合组成。通过这个大的模拟空间，我们提出了一个详细的分析的影响，违反假设的非参数多重比较检验的性能方面的三个错误和四个电源的措施。本研究的观察有助于根据实验要求和条件确定最佳的非参数检验。当违反方差分析的某些假设且组数较少时，使用逐步Steel-Dwass程序和霍尔姆方法可将I类误差控制在所需水平。邓恩的方法应采用更多的群体。当子群不平衡且群数较小时，Nemenyi方法与邓肯方法可得到高幂次值.科诺菲尔的方法成功地提供了具有少量不平衡组或具有大量平衡或不平衡组的高功率值。同时，科诺菲尔的程序无法控制I类错误率。
In this article, an extensive Monte Carlo simulation study is conducted to evaluate and compare nonparametric multiple comparison tests under violations of classical analysis of variance assumptions. Simulation space of the Monte Carlo study is composed of 288 different combinations of balanced and unbalanced sample sizes, number of groups, treatment effects, various levels of heterogeneity of variances, dependence between subgroup levels, and skewed error distributions under the single factor experimental design. By this large simulation space, we present a detailed analysis of effects of the violations of assumptions on the performance of nonparametric multiple comparison tests in terms of three error and four power measures. Observations of this study are beneficial to decide the optimal nonparametric test according to requirements and conditions of undertaken experiments. When some of the assumptions of analysis of variance are violated and number of groups is small, use of stepwise Steel-Dwass procedure with Holm's approach is appropriate to control type I error at a desired level. Dunn's method should be employed for greater number of groups. When subgroups are unbalanced and number of groups is small, Nemenyi's procedure with Duncan's approach produces high power values. Conover's procedure successfully provides high power values with a small number of unbalanced groups or with a greater number of balanced or unbalanced groups. At the same time, Conover's procedure is unable to control type I error rates.