Empirical likelihood test for high-dimensional two-sample model

TL;DR

This paper introduces an empirical likelihood-based nonparametric test for detecting changes in coefficients of high-dimensional linear models, accommodating increasing model variables and providing asymptotic normality results.

Contribution

It proposes a new empirical likelihood test for high-dimensional models with increasing variables, with proven asymptotic normality and divergence under alternatives.

Findings

Asymptotic normality of the test statistic under null hypothesis.

Divergence of the test statistic under alternative hypothesis.

Monte-Carlo simulations validate the test's performance.

Abstract

A non parametric method based on the empirical likelihood is proposed for detecting the change in the coefficients of high-dimensional linear model where the number of model variables may increase as the sample size increases. This amounts to testing the null hypothesis of no change against the alternative of one change in the regression coefficients. Based on the theoretical asymptotic behaviour of the empirical likelihood ratio statistic, we propose, for a fixed design, a simpler test statistic, easier to use in practice. The asymptotic normality of the proposed test statistic under the null hypothesis is proved, a result which is different from the $\chi^2$ law for a model with a fixed variable number. Under alternative hypothesis, the test statistic diverges. We can then find the asymptotic confidence region for the difference of parameters of the two phases. Some Monte-Carlo simulations study the behaviour of the proposed test statistic.