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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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    1. Francesco Battaglia & Lia Orfei, 2005. "Outlier Detection And Estimation In NonLinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(1), pages 107-121, January.
    2. Hai‐Bin Wang, 2005. "Parameter Estimation and Subset Selection for Separable lower Triangular Bilinear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(5), pages 743-757, September.
    3. Wing‐Kam Fung & Zhong‐Yi Zhu & Bo‐Cheng Wei & Xuming He, 2002. "Influence diagnostics and outlier tests for semiparametric mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 565-579, August.
    4. Gregor Gregorčič & Gordon Lightbody, 2012. "Gaussian process internal model control," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(11), pages 2079-2094.
    5. 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.
    6. 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.
    7. Shamim Pakzad & Guilherme Rocha & Bin Yu, 2011. "Distributed modal identification using restricted auto regressive models," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(9), pages 1473-1489.
    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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