论文检索

多变量与重复测量设计中秩检验的统一方法AI 解读

A unified approach to asymptotic rank tests is presented for a wide class of univariate and multivariate models, including repeated measures designs without compound symmetry. The models are assumed to be balanced and complete with more than one replication per cell. The proposed rank tests are constructed by ranking all of the observations together regardless of row, column, or component membership. This method of ranking offers increased power over the method of…

关于交互作用检验中秩变换的一则注记AI 解读

SUMMARY The asymptotic properties of the rank transform statistic for testing for interaction in a balanced two-way classification are studied. Necessary and sufficient conditions are obtained for the asymptotic distribution of this rank transform statistic to be chi-squared under the null hypothesis of no interaction. It is shown that the rank transform test statistic for interaction is asymptotically chi-squared, divided by its degrees of freedom, when there are exactly…

重复测量设计中区组间秩统计量的效率AI 解读

For factorial designs, tests that rely on the method of n rankings, for example, the Friedman test, suffer a potential loss of power since they depend only on within-block ranks. This deficiency is overcome by ranking across blocks as is done in the aligned rank tests and the rank transform procedures. This article examines a broad class of powerful interblock rank statistics for testing for both treatment effects and ordered alternatives and for performing multiple…

无交互双因素模型中秩变换统计量的效能AI 解读

To test for treatment effects in a two-way model when the classical assumptions of normality of errors and constancy of variance cannot be verified, Hora and Conover (1984) proposed a rank test in which the entire data set is ranked, the ranks are scored, and then the classical analysis of variance F statistic is applied to the scored ranks. They showed that the limiting null distribution of this test statistic is a chi-squared distribution divided by its degrees of freedom.…