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Detecting change-points in Markov chains

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  • Polansky, Alan M.

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  • Polansky, Alan M., 2007. "Detecting change-points in Markov chains," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6013-6026, August.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:12:p:6013-6026
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    Cited by:

    1. Bruno Damásio & João Nicolau, 2020. "Time Inhomogeneous Multivariate Markov Chains: Detecting and Testing Multiple Structural Breaks Occurring at Unknown," Working Papers REM 2020/0136, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    2. Lu Shaochuan, 2020. "Bayesian multiple changepoints detection for Markov jump processes," Computational Statistics, Springer, vol. 35(3), pages 1501-1523, September.
    3. Guglielmo D’Amico & Ada Lika & Filippo Petroni, 2019. "Change point dynamics for financial data: an indexed Markov chain approach," Annals of Finance, Springer, vol. 15(2), pages 247-266, June.
    4. Olsen, Lena Ringstad & Chaudhuri, Probal & Godtliebsen, Fred, 2008. "Multiscale spectral analysis for detecting short and long range change points in time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3310-3330, March.
    5. Guglielmo D'Amico & Filippo Petroni & Philippe Regnault & Stefania Scocchera & Loriano Storchi, 2019. "A copula based Markov Reward approach to the credit spread in European Union," Papers 1902.00691, arXiv.org.
    6. Damásio, Bruno & Nicolau, João, 2024. "Time inhomogeneous multivariate Markov chains: Detecting and testing multiple structural breaks occurring at unknown dates," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    7. Guglielmo D'Amico & Ada Lika & Filippo Petroni, 2018. "Indexed Markov Chains for financial data: testing for the number of states of the index process," Papers 1802.01540, arXiv.org.

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