Bounded overestimation of the number of factors in PCA preserves √T-valid inference and consistent factor-space recovery under a random-matrix local law.
arXiv preprint arXiv:2406.13635 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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LAE-EnKF learns a nonlinear encoder-decoder pair plus a stable linear latent dynamics operator so that the Kalman filter can be applied entirely in latent coordinates.
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Fixed-order PCA: Theory for Overestimated Factor Models
Bounded overestimation of the number of factors in PCA preserves √T-valid inference and consistent factor-space recovery under a random-matrix local law.