A scale-invariant test for linear hypothesis of means in high dimensions

IF 1.2 3区 数学 Q2 STATISTICS & PROBABILITY
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引用次数: 0

Abstract

In this paper, we propose a new scale-invariant test for linear hypothesis of mean vectors with heteroscedasticity in high-dimensional settings. Most existing tests impose strong conditions on covariance matrices so that null distributions of their tests are asymptotically normal, which restricts the application of test procedures. However, our proposed test has different null distributions under mild conditions. Additionally, the well-known Welch-Satterthwaite chi-square approximation we adopted can automatically mimic the shapes of the null distributions of the test statistic. The performances of the test are illustrated by simulation and real data in finite samples which show that it has robustness and is more powerful than three competitors.

高维度均值线性假设的标度不变检验
摘要 本文提出了一种新的规模不变检验方法,用于检验高维环境下具有异方差性的均值向量线性假设。现有的大多数检验都对协方差矩阵施加了强条件,使其检验的空分布为渐近正态分布,这限制了检验程序的应用。然而,我们提出的检验在温和条件下具有不同的空分布。此外,我们采用的著名的韦尔奇-萨特斯韦特卡方近似法可以自动模拟检验统计量的空分布形状。我们通过模拟和有限样本中的真实数据来说明该检验的性能,结果表明它具有稳健性,而且比三个竞争对手更强大。
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来源期刊
Statistical Papers
Statistical Papers 数学-统计学与概率论
CiteScore
2.80
自引率
7.70%
发文量
95
审稿时长
6-12 weeks
期刊介绍: The journal Statistical Papers addresses itself to all persons and organizations that have to deal with statistical methods in their own field of work. It attempts to provide a forum for the presentation and critical assessment of statistical methods, in particular for the discussion of their methodological foundations as well as their potential applications. Methods that have broad applications will be preferred. However, special attention is given to those statistical methods which are relevant to the economic and social sciences. In addition to original research papers, readers will find survey articles, short notes, reports on statistical software, problem section, and book reviews.
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