The top eigenvalues and eigenvectors of sample covariance matrices under entrywise missing data follow Gaussian limits whose parameters depend on the missingness probabilities.
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Limiting eigen-structure of spiked sample covariance matrices under missing observations
The top eigenvalues and eigenvectors of sample covariance matrices under entrywise missing data follow Gaussian limits whose parameters depend on the missingness probabilities.