在指数误差的单向方差分析模型中对有序备选方案进行测试

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
Anjana Mondal, Markus Pauly, Somesh Kumar
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引用次数: 0

摘要

本文考虑了指数分布误差的单向异方差分析模型。开发了似然比检验(LRT)和两种多重比较检验,用于对有序替代方案进行检验。提出了一种参数自举(PB)方法来实施检验,并证明了其渐近准确性。广泛的模拟研究表明,即使是小样本,所有建议的检验在达到标称规模值方面都是准确的。同时,所提出的同步置信区间也能保持预设的覆盖概率。我们还将这些测试的能力与最近提出的一种相当保守的测试进行了比较。最后,我们借助三个与医学研究相关的数据集对所提出的检验进行了说明。我们开发了一个用于实现测试程序的 "R "软件包,并将其共享到开放平台 "GitHub "上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Testing against ordered alternatives in one-way ANOVA model with exponential errors

Testing against ordered alternatives in one-way ANOVA model with exponential errors

In this paper, a one-way heteroscedastic ANOVA model is considered with exponentially distributed errors. The likelihood ratio test (LRT) and two multiple comparison tests are developed for testing against ordered alternatives. A parametric bootstrap (PB) approach is proposed for implementation of tests and its asymptotic accuracy is proved. An extensive simulation study shows that all the proposed tests are accurate in terms of achieving the nominal size value, even for small samples. The proposed simultaneous confidence intervals are also seen to maintain the preassigned coverage probability. The powers of these tests are compared with a recently proposed test, which is quite conservative. Finally, the proposed tests are illustrated with the help of three data sets related to medical studies. We have developed an ‘R’ package for implementing our test procedures and shared it on the open platform ‘GitHub.’

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来源期刊
CiteScore
2.00
自引率
0.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Annals of the Institute of Statistical Mathematics (AISM) aims to provide a forum for open communication among statisticians, and to contribute to the advancement of statistics as a science to enable humans to handle information in order to cope with uncertainties. It publishes high-quality papers that shed new light on the theoretical, computational and/or methodological aspects of statistical science. Emphasis is placed on (a) development of new methodologies motivated by real data, (b) development of unifying theories, and (c) analysis and improvement of existing methodologies and theories.
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