A partially shared subspace spiked model plus RMT-backed estimation recovers shared rank/indices and optimally pools two high-dimensional covariances, including a high-dim contrastive PCA estimator.
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MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.
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Joint estimation of high-dimensional spiked covariance matrices via a partially shared subspace
A partially shared subspace spiked model plus RMT-backed estimation recovers shared rank/indices and optimally pools two high-dimensional covariances, including a high-dim contrastive PCA estimator.
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Missingness-Adaptive Factor Identification in High-Dimensional Data
MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.