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Change-point monitoring in linear models

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Author Info

  • Alexander Aue
  • Lajos Horv�th
  • Marie Hušková
  • Piotr Kokoszka
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    Abstract

    , both methods have correct asymptotic size and detect a change with probability approaching unity. The methods are illustrated and compared in a small simulation study. Copyright Royal Economic Society 2006

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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2006.00190.x
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    Bibliographic Info

    Article provided by Royal Economic Society in its journal Econometrics Journal.

    Volume (Year): 9 (2006)
    Issue (Month): 3 (November)
    Pages: 373-403

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    Handle: RePEc:ect:emjrnl:v:9:y:2006:i:3:p:373-403

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    Cited by:
    1. Aue, Alexander & Horváth, Lajos & Hušková, Marie, 2012. "Segmenting mean-nonstationary time series via trending regressions," Journal of Econometrics, Elsevier, vol. 168(2), pages 367-381.
    2. Marie Hušková & Claudia Kirch, 2012. "Bootstrapping sequential change-point tests for linear regression," Metrika, Springer, vol. 75(5), pages 673-708, July.
    3. Alexander Aue & Lajos Horváth & Piotr Kokoszka & Josef Steinebach, 2008. "Monitoring shifts in mean: Asymptotic normality of stopping times," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 17(3), pages 515-530, November.
    4. Bardet, Jean-Marc & Kengne, William, 2014. "Monitoring procedure for parameter change in causal time series," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 204-221.
    5. Amitava Mukherjee, 2013. "Nonparametric Phase-II monitoring for detecting monotone trend based on inverse sampling," Statistical Methods and Applications, Springer, vol. 22(2), pages 131-153, June.
    6. Aue, Alexander & Horváth, Lajos & Reimherr, Matthew L., 2009. "Delay times of sequential procedures for multiple time series regression models," Journal of Econometrics, Elsevier, vol. 149(2), pages 174-190, April.

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