New invariant and consistent chi-squared type goodness-of-fit tests for multivariate normality and a related comparative simulation study

被引：9

作者：

Voinov, Vassilly ^{[1
,2
]}

Pya, Natalie ^{[1
]}

Makarov, Rashid ^{[1
]}

Voinov, Yevgeniy ^{[2
]}

机构：

[1] KIMEP Univ, 2 Abai Ave, Alma Ata 050010, Kazakhstan

[2] Inst Math & Math Modeling, Alma Ata, Kazakhstan

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2016年 / 45卷 / 11期

关键词：

Chi-squared goodness-of-fit tests; Invariant and consistent tests; Multivariate normality; Symmetric alternatives; Power of tests;

D O I：

10.1080/03610926.2014.901370

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

New invariant and consistent goodness-of-fit tests for multivariate normality are introduced. Tests are based on the Karhunen-Loeve transformation of a multidimensional sample from a population. A comparison of simulated powers of tests and other well-known tests with respect to some alternatives is given. The simulation study demonstrates that power of the proposed McCull test almost does not depend on the number of grouping cells. The test shows an advantage over other chi-squared type tests. However, averaged over all of the simulated conditions examined in this article, the Anderson-Darling type and the Cramer-von Mises type tests seem to be the best.

引用

页码：3249 / 3263

页数：15

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