A unified approach to testing mean vectors with large dimensions

M. Rauf Ahmad ()
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M. Rauf Ahmad: Uppsala University

AStA Advances in Statistical Analysis, 2019, vol. 103, issue 4, No 6, 593-618

Abstract: Abstract 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.

Keywords: High-dimensional inference; Behrens–Fisher problem; MANOVA; U-statistics (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10182-018-00343-z

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