A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Two sample tests for the nonparametric Behrens-Fisher problem with clustered data
Tekijät: Larocque D, Haataja R, Nevalainen J, Oja H
Kustantaja: TAYLOR & FRANCIS LTD
Julkaisuvuosi: 2010
Journal: Journal of Nonparametric Statistics
Tietokannassa oleva lehden nimi: JOURNAL OF NONPARAMETRIC STATISTICS
Lehden akronyymi: J NONPARAMETR STAT
Artikkelin numero: PII 918893090
Numero sarjassa: 6
Vuosikerta: 22
Numero: 6
Aloitussivu: 755
Lopetussivu: 771
Sivujen määrä: 17
ISSN: 1048-5252
DOI: https://doi.org/10.1080/10485250903469728
Tiivistelmä
In this paper, we consider the nonparametric Behrens-Fisher problem with cluster-correlated data. A class of weighted Mann-Whitney test statistics is introduced and studied. In particular, a comparison with other recent testing procedures for related problems is provided. The new tests are valid when the distributions do not have the same scales and/or shapes under the null hypothesis. A general class of weighted U-statistics for clustered data, encompassing the Mann-Whitney statistic, is also introduced. A simulation studies the type I error robustness and the power of the new and of some recently proposed procedures. This study shows that the incorporation of appropriate weights can greatly improve the power of the test. A real data example illustrates the use of the tests.
In this paper, we consider the nonparametric Behrens-Fisher problem with cluster-correlated data. A class of weighted Mann-Whitney test statistics is introduced and studied. In particular, a comparison with other recent testing procedures for related problems is provided. The new tests are valid when the distributions do not have the same scales and/or shapes under the null hypothesis. A general class of weighted U-statistics for clustered data, encompassing the Mann-Whitney statistic, is also introduced. A simulation studies the type I error robustness and the power of the new and of some recently proposed procedures. This study shows that the incorporation of appropriate weights can greatly improve the power of the test. A real data example illustrates the use of the tests.
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