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Nonparametric inference for stochastic linear hypotheses: Application to high-dimensional data

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  • Jeanne Kowalski
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    Abstract

    The Mann--Whitney--Wilcoxon rank sum test is limited to comparison of two groups with univariate responses. In this paper, we introduce a class of stochastic linear hypotheses that addresses these limitations within a nonparametric setting. We formulate hypotheses for simultaneous comparisons of several, multivariate response groups, without modelling the response distributions. Inference is developed based on U-statistics theory and an exchangeability assumption. The latter condition is required to identify testable hypotheses for high-dimensional response vectors, such as those arising in genomic and psychosocial research. The methodology is illustrated with two real-data applications. Copyright Biometrika Trust 2004, Oxford University Press.

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    Bibliographic Info

    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 91 (2004)
    Issue (Month): 2 (June)
    Pages: 393-408

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    Handle: RePEc:oup:biomet:v:91:y:2004:i:2:p:393-408

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