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Spiked Eigenvalues of High-Dimensional Separable Sample Covariance Matrices
2022
This paper establishes asymptotic properties for spiked empirical eigenvalues of sample covariance matrices for high-dimensional data with both cross-sectional dependence and a dependent sample structure. A new finding from the established theoretical results is that spiked empirical eigenvalues will reflect the dependent sample structure instead of the cross-sectional structure under some scenarios, which indicates that principal component analysis (PCA) may provide inaccurate inference for
doi:10.26180/21523446
fatcat:v5bgzqpuh5hd3j4kzo3dvccppq