Abstract: In this article, we consider change point inference for high-dimensional linear models. For change point detection, given any subgroup of variables, we propose a new method for testing the homogeneity of corresponding regression coefficients across the observations. Under some regularity conditions, the proposed new testing procedure controls the type I error asymptotically and is powerful against sparse alternatives and enjoys certain optimality. For change point identification, an “argmax” based change point estimator is proposed which is shown to be consistent for the true change point location. Moreover, combining with the binary segmentation technique, we further extend our new method for detecting and identifying multiple change points. Extensive numerical studies justify the validity of our new method and demonstrate its competitive performance.

Key words and phrases: Change point inference, high dimensions, linear regression, multiplier bootstrap, subgroups.