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Synthetic detection of change point and outliers in bilinear time series models

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  • Ping Chen
  • Jing Yang
  • Linyuan Li

Abstract

This paper proposes a procedure of synthetic detection for the location of a change point and outliers in bilinear time series models with a change after an unknown time point. Based on Bayesian framework, we first derive the conditional posterior distribution of the change point and from that distribution estimate the position of the change point. Then we use these results to detect the outliers in the time series before and after that change point via Gibbs sampler algorithm. Our simulation studies show that the proposed procedure is effective.

Suggested Citation

  • Ping Chen & Jing Yang & Linyuan Li, 2015. "Synthetic detection of change point and outliers in bilinear time series models," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(2), pages 284-293, January.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:2:p:284-293
    DOI: 10.1080/00207721.2013.777983
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    References listed on IDEAS

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    6. Chen, Cathy W. S., 1997. "Detection of additive outliers in bilinear time series," Computational Statistics & Data Analysis, Elsevier, vol. 24(3), pages 283-294, May.
    7. Jin-Wei Liang & Shy-Leh Chen & Ching-Ming Yen, 2013. "Identification and verification of chaotic dynamics in a missile system from experimental time series," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(4), pages 700-713.
    8. Zheng-Guang Wu & Peng Shi & Hongye Su & Jian Chu, 2012. "Delay-dependent stability analysis for discrete-time singular Markovian jump systems with time-varying delay," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(11), pages 2095-2106.
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    Cited by:

    1. Nan Li & Xunwen Zhao & Hailin Mu & Yimeng Li & Jingru Pang & Yuqing Jiang & Xin Jin & Zhenwei Pei, 2020. "Research on the Self-Repairing Model of Outliers in Energy Data Based on Regional Convergence," Energies, MDPI, vol. 13(18), pages 1-13, September.

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