Testing for Change Points in Time Series

By Xiaofeng Shao, Xianyang Zhang. 2010

269 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6a553c3676fefbab7bda3ae8f4666def3ac7d2d4

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Citations in the corpus

Testing relevant hypotheses in functional time series via self‐normalization (2018, methodology): “For this purpose we modify the classical approaches to self-normalization-based testing that was proposed by Shao (2010) and Shao and Zhang (2010) to make them applicable to testing relevant hypotheses.”
Structural breaks in time series (2013, background): “Remedies have been offered by Altissimo and Corradi (2003), Juhl and Xiao (2009), and Shao and Zhang (2010).”
Heteroscedasticity and Autocorrelation Robust Structural Change Detection (2013, methodology): “For structure change detection problems in general, the constants for the optimal window size are difficult to derive or estimate.”
Uniform change point tests in high dimension (2015, methodology): “One way to overcome this problem is to use self-normalization, as proposed in [46].”
Testing the structural stability of temporally dependent functional observations and application to climate projections (2011, methodology): “On the other hand, it is specially designed for the change point testing problem, and it has been shown very effective in eliminating the nonmonotonic power problem in the univariate time series setting by Shao and Zhang [23].”
Group LASSO for Structural Break Time Series (2014, background): “Information about these applications can be found in Basseville and Benveniste (1983), Sclove (1983), Adak and Sarkar (1996), Andreou and Ghysels (2008), Shao and Zhang (2010), and references therein.”