Nonparametric Change Point Detection in Regression

arXiv1903.02603MaRDI QIDQ6315180

Author name not available (Why is that?)

Publication date: 6 March 2019

Abstract: This paper considers the prominent problem of change-point detection in regression. The study suggests a novel testing procedure featuring a fully data-driven calibration scheme. The method is essentially a black box, requiring no tuning from the practitioner. The approach is investigated from both theoretical and practical points of view. The theoretical study demonstrates proper control of first-type error rate under $H_0$ and power approaching $1$ under $H_1$. The experiments conducted on synthetic data fully support the theoretical claims. In conclusion, the method is applied to financial data, where it detects sensible change-points. Techniques for change-point localization are also suggested and investigated.

Has companion code repository: https://github.com/akopich/gpcd

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