We propose a new nonparametric test based on the rank difference between the paired sample for testing the equality of the marginal distributions from a bivariate distribution. We also consider a modification of the n...We propose a new nonparametric test based on the rank difference between the paired sample for testing the equality of the marginal distributions from a bivariate distribution. We also consider a modification of the novel nonparametric test based on the test proposed by Baumgartern, Weiβ, and Schindler (1998). An extensive numerical power comparison for various parametric and nonparametric tests was conducted under a wide range of bivariate distributions for small sample sizes. The two new nonparametric tests have comparable power to the paired t test for the data simulated from bivariate normal distributions, and are generally more powerful than the paired t test and other commonly used nonparametric tests in several important bivariate distributions.展开更多
目的：探讨配对二项资料两组率差置信区间的估计方法,并从中进行优选推荐。方法：按照方差估计反推（method of variance of estimates recovery,MOVER）原理,将两配对组率的关联Ф系数与单组率的置信区间进行组合,构建两组率差的置信...目的：探讨配对二项资料两组率差置信区间的估计方法,并从中进行优选推荐。方法：按照方差估计反推（method of variance of estimates recovery,MOVER）原理,将两配对组率的关联Ф系数与单组率的置信区间进行组合,构建两组率差的置信区间估计方法。其中,单组率置信区间估计分别采用Wilson计分法、Agresti-Coull法（AC法）、Jeffreys法和Clopper-Pearson精确法（CP法）。借助Monte Carlo模拟实验比较不同方法的统计学性能,在不同参数设定下进行I类错误率和把握度的模拟实验：（1）设定两组率关联Ф系数为0、0.2、0.4、0.6,样本量为20、60、100,分别模拟不同率水平下各方法的I类错误率,判定其模拟I类错误率是否接近事先定义的检验水平。（2）设定关联Ф系数为0.3,两组率差为10%,分别模拟不同率水平下各方法不同样本含量下的把握度（power）变化趋势。结果：在基于单组置信区间组合估计的几种MOVER方法中,MOVER Wilson计分法、MOVER Jeffreys法的I类错误率更接近事先设定的水平,尤其是在靠近0和100%两端时,MOVER Jeffreys法的I类错误率更优;除MOVER CP法,其他3种方法的把握度接近。结论：对于配对二项资料两组率差的置信区间估计,一般情况下（两组率在20%~80%范围内）,可选择MOVER Wilson计分法或MOVER Jeffreys法;当两组率靠近两端时,推荐使用MOVER Jeffreys法。展开更多
文摘We propose a new nonparametric test based on the rank difference between the paired sample for testing the equality of the marginal distributions from a bivariate distribution. We also consider a modification of the novel nonparametric test based on the test proposed by Baumgartern, Weiβ, and Schindler (1998). An extensive numerical power comparison for various parametric and nonparametric tests was conducted under a wide range of bivariate distributions for small sample sizes. The two new nonparametric tests have comparable power to the paired t test for the data simulated from bivariate normal distributions, and are generally more powerful than the paired t test and other commonly used nonparametric tests in several important bivariate distributions.
