The minimax rate for estimating d-th order moment tensors is sqrt(p/n) wedge 1, while low-degree evidence shows detection of vanishing cumulants is hard for n much less than p to the d/2, creating a reverse detection-estimation gap.
Independent component analysis, a new concept?Signal processing, 36(3):287–314
3 Pith papers cite this work. Polarity classification is still indexing.
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Riemannian archetypal analysis projects data onto a manifold of geodesically convex archetype combinations via pullback geometry on deformed star distributions.
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Detection Is Harder Than Estimation in Certain Regimes: Inference for Moment and Cumulant Tensors
The minimax rate for estimating d-th order moment tensors is sqrt(p/n) wedge 1, while low-degree evidence shows detection of vanishing cumulants is hard for n much less than p to the d/2, creating a reverse detection-estimation gap.
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Riemannian Archetypal Analysis: Interpretable non-linear data analysis on deformed star distributions
Riemannian archetypal analysis projects data onto a manifold of geodesically convex archetype combinations via pullback geometry on deformed star distributions.
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Preserving Temporal Dynamics in Time Series Generation
An MCMC framework enforces empirical transition laws on GAN outputs to reduce temporal drift in synthetic multivariate time series.