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Some tests for detecting trends based on the modified Baumgartner- Wei?-Schindler statistics

机译:基于改进的Baumgartner-Wei-Schindler统计量的一些趋势检测测试

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摘要

We propose a modified nonparametric Baumgartner-Wei?-Schindler test and investigate its use in testing for trends among K binomial populations. Exact conditional and unconditional approaches to p-value calculation are explored in conjunction with the statistic in addition to a similar test statistic proposed by Neuh?user (2006), the unconditional approaches considered including the maximization approach (Basu, 1977), the confidence interval approach (Berger and Boos, 1994), and the E+M approach (Lloyd, 2008). The procedures are compared with regard to actual Type I error and power and examples are provided. The conditional approach and the E+M approach performed well, with the E+M approach having an actual level much closer to the nominal level. The E+M approach and the conditional approach are generally more powerful than the other p-value calculation approaches in the scenarios considered. The power difference between the conditional approach and the E+M approach is often small in the balance case. However, in the unbalanced case, the power comparison between those two approaches based on our proposed test statistic show that the E+M approach has higher power than the conditional approach.
机译:我们提出了一种改进的非参数Baumgartner-Wei?-Schindler检验,并研究了其在检验K二项式人口趋势中的用途。除了Neuhuser(2006)提出的类似检验统计量外,还结合统计量探索了精确的有条件和无条件方法进行p值计算,所考虑的无条件方法包括最大化方法(Basu,1977),置信区间方法(Berger和Boos,1994年)和E + M方法(Lloyd,2008年)。比较了有关实际I类错误和功耗的过程,并提供了示例。有条件方法和E + M方法的效果很好,其中E + M方法的实际水平更接近标称水平。在考虑的方案中,E + M方法和条件方法通常比其他p值计算方法更强大。在平衡情况下,有条件方法与E + M方法之间的功效差异通常很小。但是,在不平衡的情况下,根据我们提出的测试统计数据,这两种方法之间的功效比较表明,E + M方法比有条件方法具有更高的功效。

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