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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2 Pith papers cite this work. Polarity classification is still indexing.
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Introduces interval graphical lasso to estimate a shared precision matrix for interval-valued data and proves its sparsity and consistency.
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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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Estimating Precision Matrices for High-Dimensional Interval-Valued Data
Introduces interval graphical lasso to estimate a shared precision matrix for interval-valued data and proves its sparsity and consistency.