Gradual variance change point detection with a smoothly changing mean trend

Liang, Wanfeng; Xu, Libai

doi:10.1002/sta4.327

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…We compare the performance of the method CGCP in this paper with the method proposed in Liang and Xu (2020) called SGCP. Further, we displayed the performance in estimating

false(f_{0}, σ_{0}^{2}, δ_{T} false)

with different assigned α values.…”

Section: Simulations and Applicationmentioning

confidence: 99%

“…We would like to point out that Liu, Zhao, and Xue (2017) only gives the consistency of the changepoint location estimation under the condition that all the other important parameters such as the mean function are constant in each panel, which makes the applications of the model very limited. Another related method, Liang and Xu (2020), is designed to detect the gradual variance breakpoint for a single data sequence in the context of a smoothly changing mean function, and they give an example to display that using the method designed for detecting the abrupt variance changepoint to tackle the gradual changepoint problem resulted in the catastrophic results. We give the results of comparison between the method in Liang and Xu (2020) called SGCP and our method called CGCP in Section 3 to show the proposed method's clearly outstanding performances.…”

mentioning

confidence: 99%

“…Another related method, Liang and Xu (2020), is designed to detect the gradual variance breakpoint for a single data sequence in the context of a smoothly changing mean function, and they give an example to display that using the method designed for detecting the abrupt variance changepoint to tackle the gradual changepoint problem resulted in the catastrophic results. We give the results of comparison between the method in Liang and Xu (2020) called SGCP and our method called CGCP in Section 3 to show the proposed method's clearly outstanding performances.…”

mentioning

confidence: 99%

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Joint estimation of gradual variance changepoint for panel data with common structures

Liang

Guo

2021

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This paper considers the problem of detecting the common gradual changepoint in panel data. Given the situation that each panel/series has the common gradual variance changepoint along with a smoothly functional mean, our focus is on improving the changepoint location estimation accuracy assuming a prior that a single change has occurred. We developed an iterative algorithm motivated by the idea of divide-and-conquer for tackling this problem. In addition, we have established the rates of convergence for all the parameter estimates. Monte Carlo simulation results illustrate the effectiveness and improvement of the proposed method over the existing methods. The method is also demonstrated through an analysis on the daily returns of S&P 500 Index and NASDAQ-100 Index over a period of time.

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“…We compare the performance of the method CGCP in this paper with the method proposed in Liang and Xu (2020) called SGCP. Further, we displayed the performance in estimating

false(f_{0}, σ_{0}^{2}, δ_{T} false)

with different assigned α values.…”

Section: Simulations and Applicationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Joint estimation of gradual variance changepoint for panel data with common structures

Liang

Guo

2021

Stat

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“…Refs. [1,2] explore the detection of abrupt variance changes within the context of a smooth and continuous mean-shifting trend. To the best of our knowledge, there is a gap in the existing research landscape when detecting abrupt mean changes under a scenario with slow variance shifts and smooth variations in both mean and variance.…”

Section: Introductionmentioning

confidence: 99%

A Selective Review on Information Criteria in Multiple Change Point Detection

Gao,

Xiao,

Fang

et al. 2024

Entropy

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Change points indicate significant shifts in the statistical properties in data streams at some time points. Detecting change points efficiently and effectively are essential for us to understand the underlying data-generating mechanism in modern data streams with versatile parameter-varying patterns. However, it becomes a highly challenging problem to locate multiple change points in the noisy data. Although the Bayesian information criterion has been proven to be an effective way of selecting multiple change points in an asymptotical sense, its finite sample performance could be deficient. In this article, we have reviewed a list of information criterion-based methods for multiple change point detection, including Akaike information criterion, Bayesian information criterion, minimum description length, and their variants, with the emphasis on their practical applications. Simulation studies are conducted to investigate the actual performance of different information criteria in detecting multiple change points with possible model mis-specification for the practitioners. A case study on the SCADA signals of wind turbines is conducted to demonstrate the actual change point detection power of different information criteria. Finally, some key challenges in the development and application of multiple change point detection are presented for future research work.

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