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Structural change detection problems are often encountered in analytics andeconometrics, where the performance of a model can be significantly affected byunforeseen changes in the underlying relationships. Although these problemshave a comparatively long history in statistics, the number of studies done inthe context of multivariate data under nonparametric settings is still small.In this paper, we propose a consistent method for detecting multiple structuralchanges in a system of related regressions over a large dimensional variablespace. In most applications, practitioners also do not have a prioriinformation on the relevance of different variables, and therefore, bothlocations of structural changes as well as the corresponding sparse regressioncoefficients need to be estimated simultaneously. The method combinesnonparametric energy distance minimization principle with penalized regressiontechniques. After showing asymptotic consistency of the model, we compare theproposed approach with competing methods in a simulation study. As an exampleof a large scale application, we consider structural change point detection inthe context of news analytics during the recent financial crisis period.

Non-parametric Structural Change Detection in Multivariate Systems