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A unified approach to testing mean vectors with large dimensions
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2018 (English)In: AStA Advances in Statistical Analysis, ISSN 1863-8171, E-ISSN 1863-818XArticle in journal (Refereed) In press
Place, publisher, year, edition, pages
Springer, 2018.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-371615DOI: 10.1007/s10182-018-00343-zOAI: oai:DiVA.org:uu-371615DiVA, id: diva2:1273732
Note

A unified testing framework is presented for large-dimensional mean vectors of one or several populations which may be non-normal with unequal covariance matrices. Beginning with one-sample case, the construction of tests, underlying assumptions and asymptotic theory, is systematically extended to multi-sample case. Tests are defined in terms of U-statistics-based consistent estimators, and their limits are derived under a few mild assumptions. Accuracy of the tests is shown through simulations. Real data applications, including a five-sample unbalanced MANOVA analysis on count data, are also given.

Available from: 2018-12-21 Created: 2018-12-21 Last updated: 2018-12-22

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AStA Advances in Statistical Analysis
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