 Statistical inference for multiple change‐point modelsWu Wang, Xuming He and Zhongyi Zhu Scandinavian Journal of Statistics, 2020, vol. 47, issue 4, 1149-1170 Abstract: 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. Date: 2020 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:47:y:2020:i:4:p:1149-1170 Ordering information: This journal article can be ordered from Access Statistics for this article Scandinavian Journal of Statistics is currently edited by ÿrnulf Borgan and Bo Lindqvist More articles in Scandinavian Journal of Statistics from Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association, Swedish Statistical Association |
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