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A nonparametric procedure for changepoint detection in linear regression

Author

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  • Jing Sun
  • Deepak Sakate
  • Sunil Mathur

Abstract

Changepoint detection in linear regression has many applications in climatology, bioinformatics, finance, oceanography and medical imaging. In this article, we propose a procedure to detect changepoint in linear regression based on a nonparametric method. The proposed procedure performs well for non normal error distribution and does not require the assumption of normal distribution. A simulation study is conducted to compare the performance of the proposed procedure with the existing procedure, considering the error distribution as Laplace, Student’s t, and mixture of normal distributions. The simulation study indicates that the proposed procedure outperforms its competitor. A real-life example is used to illustrate the working procedure.

Suggested Citation

  • Jing Sun & Deepak Sakate & Sunil Mathur, 2021. "A nonparametric procedure for changepoint detection in linear regression," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(8), pages 1925-1935, April.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:8:p:1925-1935
    DOI: 10.1080/03610926.2019.1657453
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    Cited by:

    1. Kang-Ping Lu & Shao-Tung Chang, 2022. "Robust Switching Regressions Using the Laplace Distribution," Mathematics, MDPI, vol. 10(24), pages 1-24, December.

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