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A numerical comparison of the normal and some saddlepoint approximations to a distribution-free test for stochastic ordering in the competing risks model

机译:竞争风险模型中随机排序的无分布检验的正态和鞍点近似的数值比较

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Calculating the exact critical value of the test statistic is important in nonparametric statistics. However, to evaluate the exact critical value is difficult when the sample sizes are moderate to large. Under these circumstances, to consider more accurate approximation for the distribution function of a test statistic is extremely important. A distribution-free test for stochastic ordering in the competing risks model has been proposed by Bagai et al. (1989). Herein, we performed a saddlepoint approximation in the upper tails for the Bagai statistic under finite sample sizes. We then compared the saddlepoint approximations with the Bagai approximation and investigate the accuracy of the approximations. Additionally, the orders of errors of the saddlepoint approximations were derived.
机译:在非参数统计中,计算检验统计的确切临界值很重要。但是,当样本大小为中到大时,很难评估确切的临界值。在这种情况下,考虑更精确地近似检验统计量的分布函数非常重要。 Bagai等人提出了在竞争风险模型中随机排序的无分布检验。 (1989)。在本文中,我们在有限样本量下对Bagai统计量的上尾执行了鞍点近似。然后,我们将鞍点近似与Bagai近似进行比较,并研究近似的准确性。此外,得出了鞍点近似的误差阶数。

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