Empirical likelihood test for high-dimensional two-sample model

• Propose a test for detecting the change in a high-dimensional model.
• A simpler test statistic is obtained, easier to use in practice.
• Asymptotic distribution is different from classical result when the number of model variables is fixed.

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 χ2χ2 law for a model with a fixed variable number. Under alternative hypothesis, the test statistic diverges. Some Monte-Carlo simulations study the behaviour of the proposed test statistic.