Statistical inference for multiple change-point models

Wu Wang, Xuming He, Zhongyi Zhu

Research output: Contribution to journalArticlepeer-review


In this article, we propose a new technique for constructing confidence intervals for the mean of a noisy sequence with multiple change-points. We use the weighted bootstrap to generalize the bootstrap aggregating or bagging estimator. A standard deviation formula for the bagging estimator is introduced, based on which smoothed confidence intervals are constructed. To further improve the performance of the smoothed interval for weak signals, we suggest a strategy of adaptively choosing between the percentile intervals and the smoothed intervals. A new intensity plot is proposed to visualize the pattern of the change-points. We also propose a new change-point estimator based on the intensity plot, which has superior performance in comparison with the state-of-the-art segmentation methods. The finite sample performance of the confidence intervals and the change-point estimator are evaluated through Monte Carlo studies and illustrated with a real data example.
Original languageEnglish (US)
JournalScandinavian Journal of Statistics
StatePublished - Apr 2 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors thank the associate editor and two anonymous reviewers for constructive com-ments. This work was partially supported by the Natural Science Foundation of China (11671096,11731011, and 11690013).


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