矩阵变量正态性的可视化评估

Pub Date : 2023-06-17 DOI:10.1111/anzs.12388
Nikola Počuča, Michael P.B. Gallaugher, Katharine M. Clark, Paul D. McNicholas
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引用次数: 1

摘要

近年来,对三元数据的分析在文献中变得越来越普遍。通过矩阵变量分布来分析这些数据变得越来越普遍,其中最普遍的是矩阵变量正态分布。尽管存在许多评估多元正态性的方法,但评估矩阵多元正态的方法相对较少。本文提出了一种新的视觉方法,通过距离-距离图来评估矩阵变量的正态性。此外,还讨论了与所提出的视觉方法一起使用的测试程序。通过模拟数据以及在手写数字分析中的应用,说明了所提出的方法。
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Visual assessment of matrix-variate normality

In recent years, the analysis of three-way data has become ever more prevalent in the literature. It is becoming increasingly common to analyse such data by means of matrix-variate distributions, the most prevalent of which is the matrix-variate normal distribution. Although many methods exist for assessing multivariate normality, there is a relative paucity of approaches for assessing matrix-variate normality. Herein, a new visual method is proposed for assessing matrix-variate normality by means of a distance–distance plot. In addition, a testing procedure is discussed to be used in tandem with the proposed visual method. The proposed approach is illustrated via simulated data as well as an application on analysing handwritten digits.

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