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.
The Annals of Statistics , volume=
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DC-TNN decomposes tensors into low-rank core plus sparse refinement fed to coupled neural channels, yielding non-asymptotic risk bounds and the first distribution-free conformal procedure for selecting among tensor decompositions.
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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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Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection
DC-TNN decomposes tensors into low-rank core plus sparse refinement fed to coupled neural channels, yielding non-asymptotic risk bounds and the first distribution-free conformal procedure for selecting among tensor decompositions.