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arXiv · 2102.05761

On high-dimensional wavelet eigenanalysis

Patrice Abry·B. Cooper Boniece·Gustavo Didier·Herwig Wendt
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Abstract

In this paper, we characterize the asymptotic and large scale behavior of the eigenvalues of wavelet random matrices in high dimensions. We assume that possibly non-Gaussian, finite-variance $p$-variate measurements are made of a low-dimensional $r$-variate ($r \ll p$) fractional stochastic process with non-canonical scaling coordinates and in the presence of additive high-dimensional noise. The measurements are correlated both time-wise and between rows. We show that the $r$ largest eigenvalues of the wavelet random matrices, when appropriately rescaled, converge in probability to scale-invariant functions in the high-dimensional limit. By contrast, the remaining $p-r$ eigenvalues remain bounded in probability. Under additional assumptions, we show that the $r$ largest log-eigenvalues of wavelet random matrices exhibit asymptotically Gaussian distributions. The results have direct consequences for statistical inference.

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Patrice Abry, B. Cooper Boniece, Gustavo Didier, Herwig Wendt. 2024-06-09. On high-dimensional wavelet eigenanalysis. https://arxiv.org/abs/2102.05761

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