Carmen Pardo M, Lu Ying, Franco-Pereira Alba M
Department of Statistics and Operational Research, Universidad Complutense de Madrid, Madrid, Spain.
Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, USA.
J Appl Stat. 2020 Jul 23;49(1):24-43. doi: 10.1080/02664763.2020.1796944. eCollection 2022.
Several methods for comparing populations have been proposed in the literature. These methods assess the same null hypothesis of equal distributions but differ in the alternative hypothesis they consider. We focus on two important alternative hypotheses: monotone and umbrella ordering. Two new families of test statistics are proposed, including two known tests, as well as two new powerful tests under monotone ordering. Furthermore, these families are adapted for testing umbrella ordering. We compare some members of the families with respect to power and Type I errors under different simulation scenarios. Finally, the methods are illustrated in several applications to real data.
文献中已经提出了几种比较总体的方法。这些方法评估相同的分布相等的原假设，但它们所考虑的备择假设不同。我们关注两个重要的备择假设：单调序和伞形序。提出了两个新的检验统计量族，包括两个已知检验，以及在单调序下的两个新的强大检验。此外，这些族适用于检验伞形序。我们在不同的模拟场景下比较了这些族的一些成员在功效和I型错误方面的表现。最后，通过几个实际数据应用说明了这些方法。
