Establishes asymptotic consistency of factor estimates and √T-normality in factor-augmented regressions for fixed R ≥ r using anisotropic local laws from random matrix theory.
Statistica Sinica , volume=
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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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Fixed-order PCA: Theory for Overestimated Factor Models
Establishes asymptotic consistency of factor estimates and √T-normality in factor-augmented regressions for fixed R ≥ r using anisotropic local laws from random matrix theory.
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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.