WIAS Preprint No. 2687, (2020)

Nonparametric change point detection in regression


  • Avanesov, Valeriy

2010 Mathematics Subject Classification

  • 62M10 62H15


  • Bootstrap, change point detection, nonparametrics, regression, multiscale




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 H0 and power approaching 1 under H1. 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

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