用斯坦方法求得弗里德曼统计量的卡方近似的边界

IF 1.5 2区 数学 Q2 STATISTICS & PROBABILITY
Bernoulli Pub Date : 2021-11-01 DOI:10.3150/22-bej1530
Robert E. Gaunt, G. Reinert
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引用次数: 8

摘要

Friedman卡方检验是对$n\ge1$试验中$r\geq2$治疗的非参数统计检验,用于评估没有治疗效果的零假设。我们使用具有可交换对耦合的Stein方法来导出Friedman统计量的分布与其极限卡方分布之间的距离的显式界,该距离是使用光滑测试函数测量的。我们的界是最优阶$n^{-1}$,并且对参数$r$也有最优依赖性,因为当且仅当$r/n\rightarrow0$时,界趋于零。从这个界,我们推导出一个Kolmogorov距离界,它在较弱的条件$r^{1/2}/n\rightarrow0$下衰变为零。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bounds for the chi-square approximation of Friedman’s statistic by Stein’s method
Friedman's chi-square test is a non-parametric statistical test for $r\geq2$ treatments across $n\ge1$ trials to assess the null hypothesis that there is no treatment effect. We use Stein's method with an exchangeable pair coupling to derive an explicit bound on the distance between the distribution of Friedman's statistic and its limiting chi-square distribution, measured using smooth test functions. Our bound is of the optimal order $n^{-1}$, and also has an optimal dependence on the parameter $r$, in that the bound tends to zero if and only if $r/n\rightarrow0$. From this bound, we deduce a Kolmogorov distance bound that decays to zero under the weaker condition $r^{1/2}/n\rightarrow0$.
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来源期刊
Bernoulli
Bernoulli 数学-统计学与概率论
CiteScore
3.40
自引率
0.00%
发文量
116
审稿时长
6-12 weeks
期刊介绍: BERNOULLI is the journal of the Bernoulli Society for Mathematical Statistics and Probability, issued four times per year. The journal provides a comprehensive account of important developments in the fields of statistics and probability, offering an international forum for both theoretical and applied work. BERNOULLI will publish: Papers containing original and significant research contributions: with background, mathematical derivation and discussion of the results in suitable detail and, where appropriate, with discussion of interesting applications in relation to the methodology proposed. Papers of the following two types will also be considered for publication, provided they are judged to enhance the dissemination of research: Review papers which provide an integrated critical survey of some area of probability and statistics and discuss important recent developments. Scholarly written papers on some historical significant aspect of statistics and probability.
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