Regime variance testing - a quantile approach

TL;DR

This paper introduces a distribution-free, quantile-based method for detecting regime changes in time series, validated through simulations and plasma physics data.

Contribution

It develops a new estimation procedure for identifying change points and proposes three tests for regime detection using empirical quantiles, extending existing approaches.

Findings

Method effectively detects regime changes without distribution assumptions.

Validated on simulated data showing high accuracy.

Applied to plasma physics data confirming practical utility.

Abstract

This paper is devoted to testing time series that exhibit behavior related to two or more regimes with different statistical properties. Motivation of our study are two real data sets from plasma physics with observable two-regimes structure. In this paper we develop estimation procedure for critical point of division the structure change of a time series. Moreover we propose three tests for recognition such specific behavior. The presented methodology is based on the empirical second moment and its main advantage is lack of the distribution assumption. Moreover, the examined statistical properties we express in the language of empirical quantiles of the squared data therefore the methodology is an extension of the approach known from the literature. The theoretical results we confirm by simulations and analysis of real data of turbulent laboratory plasma.