More powerful tests for sparse high-dimensional covariances matrices

Liuhua Peng, Song Chen and Wen Zhou

Journal of Multivariate Analysis, 2016, vol. 149, issue C, 124-143

Abstract: This paper considers improving the power of tests for the identity and sphericity hypotheses regarding high dimensional covariance matrices. The power improvement is achieved by employing the banding estimator for the covariance matrices, which leads to significant reduction in the variance of the test statistics in high dimension. Theoretical justification and simulation experiments are provided to ensure the validity of the proposed tests. The tests are used to analyze a dataset from an acute lymphoblastic leukemia gene expression study for an illustration.

Keywords: Banding estimation; High-dimensional inference; Hypothesis testing; Sparse covariance matrix (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.jmva.2016.03.008

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