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A change-point problem in relative error-based regression

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  • Zhanfeng Wang
  • Wenxin Liu
  • Yuanyuan Lin

Abstract

A nonparametric relative error-based method is proposed to detect and estimate the change point for the multiplicative regression models. The asymptotic distribution of the proposed test statistic for no change-point effect is established. We prove the $$n$$ n -consistency of the proposed estimator of the change point. Simulation studies demonstrate that change-point detection and estimation with relative errors perform reasonably well in many practical situations. Application is illustrated with a financial dataset. Copyright Sociedad de Estadística e Investigación Operativa 2015

Suggested Citation

  • Zhanfeng Wang & Wenxin Liu & Yuanyuan Lin, 2015. "A change-point problem in relative error-based regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 835-856, December.
    • Handle: RePEc:spr:testjl:v:24:y:2015:i:4:p:835-856
    DOI: 10.1007/s11749-015-0438-2
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    References listed on IDEAS

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    1. Zhou Zhou, 2013. "Heteroscedasticity and Autocorrelation Robust Structural Change Detection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 726-740, June.
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    6. Chen, Kani & Guo, Shaojun & Lin, Yuanyuan & Ying, Zhiliang, 2010. "Least Absolute Relative Error Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1104-1112.
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    Cited by:

    1. Yinjun Chen & Hao Ming & Hu Yang, 2024. "Efficient variable selection for high-dimensional multiplicative models: a novel LPRE-based approach," Statistical Papers, Springer, vol. 65(6), pages 3713-3737, August.
    2. Zhanfeng Wang & Zhuojian Chen & Zimu Chen, 2018. "H-relative error estimation for multiplicative regression model with random effect," Computational Statistics, Springer, vol. 33(2), pages 623-638, June.

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