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• W3034455815 abstract "We consider the problem of detecting jumps in an otherwise smoothly evolving trend whilst the covariance and higher-order structures of the system can experience both smooth and abrupt changes over time. The number of jump points is allowed to diverge to infinity with the jump sizes possibly shrinking to zero. The method is based on a multiscale application of an optimal jump-pass filter to the time series, where the scales are dense between admissible lower and upper bounds. For a wide class of non-stationary time series models and associated trend functions, the proposed method is shown to be able to detect all jump points within a nearly optimal range with a prescribed probability asymptotically. For a time series of length $n$, the computational complexity of the proposed method is $O(n)$ for each scale and $O(nlog^{1+epsilon} n)$ overall, where $epsilon$ is an arbitrarily small positive constant. Simulations and data analysis show that the proposed jump testing and estimation method performs robustly and accurately under complex temporal dynamics." @default.
• W3034455815 created "2020-06-19" @default.
• W3034455815 creator A5026624461 @default.
• W3034455815 creator A5046940775 @default.
• W3034455815 date "2019-09-13" @default.
• W3034455815 modified "2023-09-28" @default.
• W3034455815 title "Multiscale Jump Testing and Estimation Under Complex Temporal Dynamics." @default.
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