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Inference for multiple change points in heavy-tailed time series via rank likelihood ratio scan statistics

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  • Chen, Zhanshou
  • Xu, Qiongyao
  • Li, Huini

Abstract

This paper proposes a rank likelihood ratio scan method for estimating multiple change points in piecewise heavy-tailed time series. It can effectively improve the estimate accuracy and solve the problem that the likelihood ratio scan method overestimates the change points in such a time series. A simulation and analyses of two sets of real data illustrate the efficiency of the method.

Suggested Citation

  • Chen, Zhanshou & Xu, Qiongyao & Li, Huini, 2019. "Inference for multiple change points in heavy-tailed time series via rank likelihood ratio scan statistics," Economics Letters, Elsevier, vol. 179(C), pages 53-56.
    • Handle: RePEc:eee:ecolet:v:179:y:2019:i:c:p:53-56
    DOI: 10.1016/j.econlet.2019.03.017
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    References listed on IDEAS

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    1. Chun Yip Yau & Zifeng Zhao, 2016. "Inference for multiple change points in time series via likelihood ratio scan statistics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 895-916, September.
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    5. Qin, Ruibing & Ma, Junjie, 2018. "An efficient algorithm to estimate the change in variance," Economics Letters, Elsevier, vol. 168(C), pages 15-17.
    6. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    7. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
    8. Chen, Zhanshou & Jin, Zi & Tian, Zheng & Qi, Peiyan, 2012. "Bootstrap testing multiple changes in persistence for a heavy-tailed sequence," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2303-2316.
    9. Marie Hušková & Simos Meintanis, 2006. "Change Point Analysis based on Empirical Characteristic Functions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 63(2), pages 145-168, April.
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