Abstract
The sample covariance matrix is a most important random matrix in multivariate statistical inference. It is fundamental in hypothesis testing, principal component analysis, factor analysis, and discrimination analysis. Many test statistics are defined by its eigenvalues.
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Bai, Z., Silverstein, J.W. (2010). Sample Covariance Matrices and the Marčenko-Pastur Law. In: Spectral Analysis of Large Dimensional Random Matrices. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0661-8_3
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DOI: https://doi.org/10.1007/978-1-4419-0661-8_3
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