A fast subsampling method for estimating the distribution of signal-to-noise ratio statistics in nonparametric time series regression models

TL;DR

This paper introduces a fast nonparametric subsampling method to estimate the distribution of SNR statistics in nonparametric time series regression models, accommodating complex noise dependencies and large datasets.

Contribution

It develops a novel subsampling approach combining smoothing and subsampling, with asymptotic guarantees, for estimating SNR distributions in complex, non-stationary noise environments.

Findings

Method effectively handles large data samples.

Provides asymptotic theoretical guarantees.

Demonstrates good finite sample performance.

Abstract

Signal-to-noise ratio (SNR) statistics play a central role in many applications. A common situation where SNR is studied is when a continuous time signal is sampled at a fixed frequency with some noise in the background. While estimation methods exist, little is known about its distribution when the noise is not weakly stationary. In this paper we develop a nonparametric method to estimate the distribution of an SNR statistic when the noise belongs to a fairly general class of stochastic processes that encompasses both short and long-range dependence, as well as nonlinearities. The method is based on a combination of smoothing and subsampling techniques. Computations are only operated at the subsample level, and this allows to manage the typical enormous sample size produced by modern data acquisition technologies. We derive asymptotic guarantees for the proposed method, and we show the finite sample performance based on numerical experiments. Finally, we propose an application to electroencephalography (EEG) data.