Generalized Nonparametric Composite Tests for High-Dimensional Data

Autor:	Xiaoli Kong, Alejandro Villasante-Tezanos, Solomon W. Harrar
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	high dimension two-sample test Wilcoxon–Mann–Whitney nonparametric α-mixing Mathematics QA1-939
Zdroj:	Symmetry, Vol 14, Iss 6, p 1153 (2022)
Druh dokumentu:	article
ISSN:	2073-8994
DOI:	10.3390/sym14061153
Popis:	In this paper, composite high-dimensional nonparametric tests for two samples are proposed, by using component-wise Wilcoxon–Mann–Whitney-type statistics. No distributional assumption, moment condition, or parametric model is required for the development of the tests and the theoretical results. Two approaches are employed, for estimating the asymptotic variance of the composite statistic, leading to two tests. In both cases, banding of the covariance matrix to estimate variance of the test statistic is involved. An adaptive algorithm, for selecting the banding window width, is proposed. Numerical studies are provided, to show the favorable performance of the new tests in finite samples and under varying degrees of dependence.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/c364be808de94028b0e83761a55a6db6 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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