Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
(1998)Decoupling
4 Pith papers cite this work. Polarity classification is still indexing.
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Establishes that the L^p(Ω; S_r) norm of a finite-order decoupled homogeneous Gaussian chaos operator is bounded by C_m (p+r)^{m/2} times the maximum oriented Schatten flattening norm of its kernel.
A functional central limit theorem for pattern frequencies in 2D samples enables nonparametric goodness-of-fit, two-sample, and symmetry tests for copulas, with bootstrap critical values and parametric examples.
Proves localized operator estimate for centered second-order Gaussian chaoses with scale window conditions, applicable to paracontrolled resonant products on R^d.
citing papers explorer
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Statistically and Computationally Optimal Estimation and Inference of Common Subspaces
Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
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Finite-Order Hilbertian Gaussian Random Tensor Estimates
Establishes that the L^p(Ω; S_r) norm of a finite-order decoupled homogeneous Gaussian chaos operator is bounded by C_m (p+r)^{m/2} times the maximum oriented Schatten flattening norm of its kernel.
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Pattern-based tests for two-dimensional copulas
A functional central limit theorem for pattern frequencies in 2D samples enables nonparametric goodness-of-fit, two-sample, and symmetry tests for copulas, with bootstrap critical values and parametric examples.
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Localized Centered Second-Chaos Operator
Proves localized operator estimate for centered second-order Gaussian chaoses with scale window conditions, applicable to paracontrolled resonant products on R^d.