MATE is a missingness-adaptive thresholding estimator that consistently identifies the number of identifiable factors in high-dimensional incomplete data without imputation.
The Annals of Statistics , volume=
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The authors relax the closeness-to-identity condition in Assumption (B) of Gusakova et al. so that central and stable limit theorems for simplex volumes hold under spiked and Toeplitz covariance structures.
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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.
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A note on "The volume of random simplices from elliptical distributions in high dimension"
The authors relax the closeness-to-identity condition in Assumption (B) of Gusakova et al. so that central and stable limit theorems for simplex volumes hold under spiked and Toeplitz covariance structures.