Inference of Breakpoints in High-dimensional Time Series

References

• Bai, J. (2010), “Common Breaks in Means and Variances for Panel Data,” Journal of Econometrics, 157, 78–92. DOI: 10.1016/j.jeconom.2009.10.020.
   Web of Science ®Google Scholar
• Bai, J., and Perron, P. (1998), “Estimating and Testing Linear Models With Multiple Structural Changes,” Econometrica, 66, 47–78. DOI: 10.2307/2998540.
   Web of Science ®Google Scholar
• Bai, J., and Perron, P. (2003), “Computation and Analysis of Multiple Structural Change Models,” Journal of Applied Econometrics, 18, 1–22.
   Web of Science ®Google Scholar
• Catoni, O. (2012), “Challenging the Empirical Mean and Empirical Variance: A Deviation Study,” Annales de l’Institut Henri Poincare (B) Probability and Statistics, 48, 1148–1185.
   Web of Science ®Google Scholar
• Chen, L., Wang, W., and Wu, W. B. (2020), “Dynamic Semiparametric Factor Model With Structural Breaks,” Journal of Business & Economic Statistics, 1–15.
   Web of Science ®Google Scholar
• Chen, L., and Wu, W. B. (2019), “Testing for Trends in High-dimensional Time Series,” Journal of the American Statistical Association, 114, 869–881. DOI: 10.1080/01621459.2018.1456935.
   Web of Science ®Google Scholar
• Chernozhukov, V., Chetverikov, D., and Kato, K. (2013), “Gaussian Approximations and Multiplier Bootstrap for Maxima of Sums of High-dimensional Random Vectors,” Annals of Statistics, 41, 2786–2819.
   Web of Science ®Google Scholar
• Chernozhukov, V., Chetverikov, D., and Kato, K. (2017), “Central Limit Theorems and Bootstrap in High Dimensions,” Annals of Probability, 45, 2309–2352.
   Web of Science ®Google Scholar
• Cho, H. (2016), “Change-point Detection in Panel Data Via Double CUSUM Statistic,” The Electronic Journal of Statistics, 10, 2000–2038. DOI: 10.1214/16-EJS1155.
   Web of Science ®Google Scholar
• Cho, H., and Fryzlewicz, P. (2015), “Multiple-change-point Detection for High Dimensional Time Series Via Sparsified Binary Segmentation,” Journal of the Royal Statistical Society, Series B, 77, 475–507. DOI: 10.1111/rssb.12079.
   Google Scholar
• Eichinger, B., and Kirch, C. (2018), “A MOSUM Procedure for the Estimation of Multiple Random Change Points,” Bernoulli, 24, 526–564. DOI: 10.3150/16-BEJ887.
   Web of Science ®Google Scholar
• Enikeeva, F., and Harchaoui, Z. (2019), “High-dimensional Change-point Detection Under Sparse Alternatives,” Annals of Statistics, 47, 2051–2079.
   Web of Science ®Google Scholar
• Fan, J., and Gijbels, I. (1996), Local Polynomial Modelling and Its Applications, Volume 66 of Monographs on Statistics and Applied Probability, London: Chapman & Hall.
   Google Scholar
• Fan, J., Li, Q., and Wang, Y. (2017), “Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions,” Journal of the Royal Statistical Society, Series B, 79, 247–265. DOI: 10.1111/rssb.12166.
   Google Scholar
• Fryzlewicz, P. (2014), “Wild Binary Segmentation for Multiple Change-point Detection,” Annals of Statistics, 42, 2243–2281.
   Web of Science ®Google Scholar
• Fryzlewicz, P. (2018), “Tail-greedy Bottom-up Data Decompositions and Fast Multiple Change-point Detection,” Annals of Statistics, 46, 3390–3421.
   Web of Science ®Google Scholar
• Hansen, B. E. (2000), Sample splitting and threshold estimation. Econometrica, 68, 575–603. DOI: 10.1111/1468-0262.00124.
   Web of Science ®Google Scholar
• Harlé, F., Chatelain, F., Gouy-Pailler, C., and Achard, S. (2016), “Bayesian Model for Multiple Change-points Detection in Multivariate Time Series,” IEEE Transaction on Signal Processing, 64, 4351–4362. DOI: 10.1109/TSP.2016.2566609.
   Web of Science ®Google Scholar
• Hušková, M., and Slabỳ, A. (2001), “Permutation Tests for Multiple Changes,” Kybernetika, 37, 605–622.
   Web of Science ®Google Scholar
• Jackson, B., Scargle, J. D., Barnes, D., Arabhi, S., Alt, A., Gioumousis, P., Gwin, E., Sangtrakulcharoen, P., Tan, L., and Tsai, T. T. (2005), “An Algorithm for Optimal Partitioning of Data on An Interval,” IEEE Signal Processing Letters, 12, 105–108. DOI: 10.1109/LSP.2001.838216.
   Web of Science ®Google Scholar
• Jirak, M. (2015), “Uniform Change Point Tests in High Dimension,” Annals of Statistics, 43, 2451–2483.
   Web of Science ®Google Scholar
• Killick, R., Fearnhead, P., and Eckley, I. A. (2012), “Optimal Detection of Changepoints With a Linear Computational Cost,” Journal of American Statistical Association, 107, 1590–1598. DOI: 10.1080/01621459.2012.737745.
   Web of Science ®Google Scholar
• Lahiri, S. N. (2003), Resampling Methods for Dependent Data, Springer Series in Statistics, New York: Springer-Verlag.
   Google Scholar
• Lee, S., Seo, M. H., and Shin, Y. (2016), “The Lasso for High Dimensional Regression With a Possible Change Point,” Journal of the Royal Statistical Society, Series B, 78, 193–210. DOI: 10.1111/rssb.12108.
   Google Scholar
• Lévy-Leduc, C., and Roueff, F. (2009), “Detection and Localization of Change-points in High-dimensional Network Traffic Data,” Annals of Applied Statistics, 3, 637–662.
   Web of Science ®Google Scholar
• Li, D., Qian, J., and Su, L. (2016), “Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks,” Journal of the American Statistical Association, 111, 1804–1819. DOI: 10.1080/01621459.2015.1119696.
   Web of Science ®Google Scholar
• Liu, H., Gao, C., and Samworth, R. J. (2019), “Minimax Rates in Sparse, High-dimensional Changepoint Detection,” arXiv preprint arXiv:1907.10012.
   Google Scholar
• Meier, A., Kirch, C., and Cho, H. (2019), “mosum: A Package for Moving Sums in Change-point Analysis,” Preprint.
   Google Scholar
• Olshen, A. B., Venkatraman, E., Lucito, R., and Wigler, M. (2004), “Circular Binary Segmentation for the Analysis of Array-based DNA Copy Number Data,” Biostatistics, 5, 557–572. DOI: 10.1093/biostatistics/kxh008.
   PubMed Web of Science ®Google Scholar
• Politis, D. N., Romano, J. P., and Wolf, M. (1999), Subsampling. Springer Series in Statistics, New York: Springer-Verlag.
   Google Scholar
• Preuss, P., Puchstein, R., and Dette, H. (2015), “Detection of Multiple Structural Breaks in Multivariate Time Series,” Journal of American Statistical Association, 110, 654–668. DOI: 10.1080/01621459.2014.920613.
   Web of Science ®Google Scholar
• Scott, A. J., and M. Knott (1974), “A Cluster Analysis Method for Grouping Means in the Analysis of Variance,” Biometrics, 30, 507–512. DOI: 10.2307/2529204.
   Web of Science ®Google Scholar
• Stryhn, H. (1996), “The Location of the Maximum of Asymmetric Two-sided Brownian Motion With Triangular Drift,” Statistics & Probability Letters, 29, 279 – 284.
   Web of Science ®Google Scholar
• Tibshirani, R., and Wang, P. (2007), “Spatial Smoothing and Hot Spot Detection for CGH Data Using the Fused Lasso,” Biostatistics, 9, 18–29. DOI: 10.1093/biostatistics/kxm013.
   PubMed Web of Science ®Google Scholar
• Wang, T., and Samworth, R. J. (2018), “High Dimensional Change Point Estimation Via Sparse Projection,” Journal of the Royal Statistical Society, Series B, 80, 57–83. DOI: 10.1111/rssb.12243.
   Google Scholar
• Wu, W., and Zhou, Z. (2019), “Mace: Multiscale Abrupt Change Estimation Under Complex Temporal Dynamics,” arXiv preprint arXiv:1909.06307. 5 The Electronic Journal of Statistics, 10, 352–379.
   Google Scholar
• Wu, W. B., and Zhao, Z. (2007), “Inference of Trends in Time Series,” Journal of the Royal Statistical Society, Series B, 69, 391–410. DOI: 10.1111/j.1467-9868.2007.00594.x.
   Google Scholar
• Zhang, D., Wu, W. B. (2017), “Gaussian Approximation for High Dimensional Time Series,” The Annals of Statistics, 45, 1895–1919. DOI: 10.1214/16-AOS1512.
   Web of Science ®Google Scholar
• Zhang, N. R., Siegmund, D. O., Ji, H., and Li, J. Z. (2010), “Detecting Simultaneous Changepoints in Multiple Sequences,” Biometrika, 97, 631–645. With supplementary data available online. DOI: 10.1093/biomet/asq025.
   PubMed Web of Science ®Google Scholar