A weighted U-statistic based change point test for multivariate time series
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s00362-022-01341-9.pdf
Reference35 articles.
1. Akbari S, Reddy MJ (2018) Detecting changes in regional rainfall series in India using binary segmentation-based multiple change-point detection techniques. In: Singh VP, Yadav S, Yadava RN (eds) Climate change impacts. Springer Nature, Singapore, pp 103–116
2. Aue A, Horváth L (2013) Structural breaks in time series. J Time Ser Anal 34(1):1–16
3. Bardet JM, Dion C (2019) Robust semi-parametric multiple change-points detection. Signal Process 156:145–155
4. Berkes I, Gombay E, Horváth L (2009) Testing for changes in the covariance structure of linear processes. J Stat Plan Inference 139(6):2044–2063
5. Betken A (2016) Testing for change-points in long-range dependent time series by means of a self-normalized Wilcoxon test. J Time Ser Anal 37(6):785–809
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