High-dimensional inference on covariance structures via the extended cross-data-matrix methodology

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Abstract

Tests of the correlation matrix between two subsets of a high-dimensional random vector are considered. The test statistic is based on the extended cross-data-matrix methodology (ECDM) and shown to be unbiased. The ECDM estimator is also proved to be consistent and asymptotically Normal in high-dimensional settings. The authors propose a test procedure based on the ECDM estimator and evaluate its size and power, both theoretically and numerically. They give several applications of the ECDM estimator and illustrate the performance of the test procedure using microarray data.

Journal

  • Journal of Multivariate Analysis

    Journal of Multivariate Analysis (151), 151-166, 2016-10

    Elsevier

Keywords

Codes

  • NII Article ID (NAID)
    120005856274
  • NII NACSIS-CAT ID (NCID)
    AA0025295X
  • Text Lang
    ENG
  • Article Type
    journal article
  • ISSN
    0047-259X
  • Data Source
    IR 
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