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• W3176332994 abstract "In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRSM) for multiple change-point inference in piecewise stationary time series, which estimates the number and locations of change-points and provides a confidence interval for each change-point. The computational complexity of using GLRSM for multiple change-point detection is as low as $O(n(log n)^{3})$ for a series of length n . Extensive simulation studies are provided to demonstrate the effectiveness of the proposed methodology under different scenarios. Applications to financial time series are also illustrated." @default.
• W3176332994 created "2021-07-05" @default.
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• W3176332994 date "2021-06-25" @default.
• W3176332994 modified "2023-09-26" @default.
• W3176332994 title "BOOTSTRAP INFERENCE FOR MULTIPLE CHANGE-POINTS IN TIME SERIES" @default.
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• W3176332994 doi "https://doi.org/10.1017/s0266466621000293" @default.
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