Wavelet eigenvalue regression in high dimensions

TL;DR

This paper introduces a wavelet eigenvalue regression method for high-dimensional, non-Gaussian measurements of low-dimensional fractional processes, demonstrating consistency and asymptotic normality, with a new estimator for the system's effective dimension.

Contribution

The paper develops a novel wavelet eigenvalue regression approach for high-dimensional data with non-Gaussian noise, including a consistent estimator for the system's effective dimension.

Findings

Method is consistent in estimating fractal structure.

Under certain conditions, the estimator is asymptotically Gaussian.

Simulation studies show good finite-sample performance.

Abstract

In this paper, we construct the wavelet eigenvalue regression methodology 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. Building upon the asymptotic and large scale properties of wavelet random matrices in high dimensions, the wavelet eigenvalue regression is shown to be consistent and, under additional assumptions, asymptotically Gaussian in the estimation of the fractal structure of the system. We further construct a consistent estimator of the effective dimension $r$ of the system that significantly increases the robustness of the methodology. The estimation performance over finite samples is studied by means of simulations.