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Volumn 101, Issue 6, 2010, Pages 1319-1329

Testing the equality of several covariance matrices with fewer observations than the dimension

Author keywords

Comparison of powers; Equality of several covariance matrices; Equality of two covariances; High dimensional data; Normality; Sample size smaller than the dimension

Indexed keywords


EID: 77949541881     PISSN: 0047259X     EISSN: 10957243     Source Type: Journal    
DOI: 10.1016/j.jmva.2009.12.010     Document Type: Article
Times cited : (114)

References (7)
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  • 2
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  • 3
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    • Testing the equality of two covariance matrices and independence of two sub-vectors with fewer observations than the dimension
    • Oct 12-14, University of North Carolina at Greensboro, NC, USA
    • M.S. Srivastava, Testing the equality of two covariance matrices and independence of two sub-vectors with fewer observations than the dimension, International Conference on Advances in Interdisciplinary Statistics and Combinatorics, Oct 12-14, 2007, University of North Carolina at Greensboro, 2007, NC, USA
    • (2007) International Conference on Advances in Interdisciplinary Statistics and Combinatorics
    • Srivastava, M.S.1
  • 4
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    • Srivastava, M.S.1
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    • Some tests concerning the covariance matrix in high-dimensional data
    • Srivastava M.S. Some tests concerning the covariance matrix in high-dimensional data. J. Japan Statist. Soc. 35 (2005) 251-272
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    • Srivastava, M.S.1
  • 7
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    • A well conditioned estimator for large-dimensional covariance matrices
    • Ledoit O., and Wolf M. A well conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88 (2004) 365-411
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    • Ledoit, O.1    Wolf, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.