Data-driven estimation of change-points with mean shift

Yang, Wenzhi; Liu, Huanshuo; Wang, Yiwei; Wang, Xuejun

doi:10.1007/s42952-022-00194-0

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…Then, based on the modified data {X * * 1 , • • • , X * * T }, we can combine the three-step algorithm with iterative methods to detect more change-points. For further details, one can refer to [20,21] and the sources detailed therein.…”

Section: The Three-step Algorithmmentioning

confidence: 99%

“…In addition to the change-point studies referred to above, many scholars have sought to extend the change-points of the mean and variance for both independent and dependent data. For the mean change-point example, in [14][15][16], CUSUM estimators were investigated with dependent errors; in [17], a weighted CUSUM estimator was studied using an infinite variance AR(p) process; in [18,19], a self-normalization method was used to test the mean change-point in a time series; in [20,21], data-driven methods were used to investigate the mean shift and variance change; in [22][23][24], CUSUM estimators were discussed regarding the mean change-point with panel data. For the variance change-point example, the Schwarz information criterion (SIC) estimator of variance change was studied with independent normal errors in [25]; ref.…”

Section: Introductionmentioning

confidence: 99%

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Combination Test for Mean Shift and Variance Change

Gao,

Shi,

Wang

et al. 2023

Symmetry

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This paper considers a new mean-variance model with strong mixing errors and describes a combination test for the mean shift and variance change. Under some stationarity and symmetry conditions, the important limiting distribution for a combination test is obtained, which can derive the limiting distributions for the mean change test and variance change test. As an application, an algorithm for a three-step method to detect the change-points is given. For example, the first step is to test whether there is at least a change-point. The second and third steps are to detect the mean change-point and the variance change-point, respectively. To illustrate our results, some simulations and real-world data analysis are discussed. The analysis shows that our tests not only have high powers, but can also determine the mean change-point or variance change-point. Compared to the existing methods of cpt.meanvar and mosum from the R package, the new method has the advantages of recognition capability and accuracy.

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Section: The Three-step Algorithmmentioning

confidence: 99%