Nearest-neighbor radii converge almost surely and obey local-dimension moment bounds under polynomial and geometric mixing dependence.
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Consistent rank correlation matrices in the proportional high-dimensional regime converge in spectral distribution to the semicircle law.
The attainable (ξ, ρ) region is a convex set with boundary from novel diagonal-band copulas; ξ ≤ |ρ| holds under stochastic monotonicity and the maximum of ρ − ξ equals 0.4.
Extends the Chatterjee-Spearman max-type test to symmetric form with derived null distribution, proves asymptotic independence from Kendall's tau and quadrant correlation, and explores multivariate cases with simulation evidence of improved power.
citing papers explorer
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Nearest-Neighbor Radii under Dependent Sampling
Nearest-neighbor radii converge almost surely and obey local-dimension moment bounds under polynomial and geometric mixing dependence.
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Limiting spectral distributions of large consistent rank correlation matrices
Consistent rank correlation matrices in the proportional high-dimensional regime converge in spectral distribution to the semicircle law.
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The exact region and an inequality between Chatterjee's and Spearman's rank correlations
The attainable (ξ, ρ) region is a convex set with boundary from novel diagonal-band copulas; ξ ≤ |ρ| holds under stochastic monotonicity and the maximum of ρ − ξ equals 0.4.
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On the extensions of the Chatterjee-Spearman test
Extends the Chatterjee-Spearman max-type test to symmetric form with derived null distribution, proves asymptotic independence from Kendall's tau and quadrant correlation, and explores multivariate cases with simulation evidence of improved power.