An Exponential Inequality for U-Statistics under Mixing Conditions

TL;DR

This paper establishes a new exponential inequality for U-statistics in time series, providing bounds similar to independent cases and applicable to non-stationary data, aiding high-dimensional time series inference.

Contribution

It introduces a novel exponential inequality for U-statistics under mixing conditions in time series, with explicit bounds and a new decomposition method.

Findings

Provides explicit mixing conditions for fast convergence.

Extends results to non-stationary time series.

Applicable to high-dimensional time series inference.

Abstract

The family of U-statistics plays a fundamental role in statistics. This paper proves a novel exponential inequality for U-statistics under the time series setting. Explicit mixing conditions are given for guaranteeing fast convergence, the bound proves to be analogous to the one under independence, and extension to non-stationary time series is straightforward. The proof relies on a novel decomposition of U-statistics via exploiting the temporal correlatedness structure. Such results are of interest in many fields where high dimensional time series data are present. In particular, applications to high dimensional time series inference are discussed.