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Detection of multiple undocumented change-points using adaptive Lasso

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  • Jie Shen
  • Colin M. Gallagher
  • QiQi Lu

Abstract

The problem of detecting multiple undocumented change-points in a historical temperature sequence with simple linear trend is formulated by a linear model. We apply adaptive least absolute shrinkage and selection operator (Lasso) to estimate the number and locations of change-points. Model selection criteria are used to choose the Lasso smoothing parameter. As adaptive Lasso may overestimate the number of change-points, we perform post-selection on change-points detected by adaptive Lasso using multivariate t simultaneous confidence intervals. Our method is demonstrated on the annual temperature data (year: 1902-2000) from Tuscaloosa, Alabama.

Suggested Citation

  • Jie Shen & Colin M. Gallagher & QiQi Lu, 2014. "Detection of multiple undocumented change-points using adaptive Lasso," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1161-1173, June.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1161-1173
    DOI: 10.1080/02664763.2013.862220
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Harchaoui, Z. & Lévy-Leduc, C., 2010. "Multiple Change-Point Estimation With a Total Variation Penalty," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1480-1493.
    3. Michael Robbins & Colin Gallagher & Robert Lund & Alexander Aue, 2011. "Mean shift testing in correlated data," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(5), pages 498-511, September.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Henri Caussinus & Olivier Mestre, 2004. "Detection and correction of artificial shifts in climate series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(3), pages 405-425, August.
    6. Thomas J. Fisher & Colin M. Gallagher, 2012. "New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 777-787, June.
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    Cited by:

    1. Behrendt, Simon & Schweikert, Karsten, 2021. "A Note on Adaptive Group Lasso for Structural Break Time Series," Econometrics and Statistics, Elsevier, vol. 17(C), pages 156-172.
    2. Shi, Xuesheng & Gallagher, Colin & Lund, Robert & Killick, Rebecca, 2022. "A comparison of single and multiple changepoint techniques for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    3. Baolong Ying & Qijing Yan & Zehua Chen & Jinchao Du, 2024. "A sequential feature selection approach to change point detection in mean-shift change point models," Statistical Papers, Springer, vol. 65(6), pages 3893-3915, August.

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