Correlation tests for high-dimensional data using extended cross-data-matrix methodology Correlation tests for high-dimensional data using extended cross-data-matrix methodology

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Abstract

In this paper, we consider tests of correlation when the sample size is much lower than the dimension. We propose a new estimation methodology called the extended cross-data-matrix methodology. By applying the method, we give a new test statistic for high-dimensional correlations. We show that the test statistic is asymptotically normal when p→∞p→∞ and n→∞n→∞. We propose a test procedure along with sample size determination to ensure both prespecified size and power for testing high-dimensional correlations. We further develop a multiple testing procedure to control both family wise error rate and power. Finally, we demonstrate how the test procedures perform in actual data analyses by using two microarray data sets.

Journal

  • Journal of multivariate analysis

    Journal of multivariate analysis (117), 313-331, 2013-05

    Elsevier

Cited by:  1

Keywords

Codes

  • NII Article ID (NAID)
    120005246720
  • NII NACSIS-CAT ID (NCID)
    AA0025295X
  • Text Lang
    ENG
  • Article Type
    Journal Article
  • ISSN
    0047-259X
  • Data Source
    CJPref  IR 
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