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Research Article

Nonparametric Shiryaev-Roberts change-point detection procedures based on modified empirical likelihood

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Received 16 Jan 2023, Accepted 10 Jan 2024, Published online: 23 Jan 2024

References

  • N. Balakrishnan, L. Bordes, C. Paroissin, and J. Turlot, Single change-point detection methods for small lifetime samples, Metrika 79 (2016), pp. 531–551.
  • I. Berkes, E. Gombay, L. Horvath, and P. Kokoszka, Sequential change-point detection in garch (p,q) models, Econ. Theory. 20 (2004), pp. 1140–1167.
  • J. Chen, A. Variyath, and B. Abraham, Adjusted empirical likelihood and its properties, J. Comput. Graph. Statist. 17 (2008), pp. 426–443.
  • W. Du, A. Polunchenko, and G. Sokolov, On robustness of the Shiryaev-Roberts change-point detection procedure under parameter misspecification in the post-change distribution, Comm. Statist. Simul. Comput. 46 (2017), pp. 2185–2206.
  • J. Gosman, T. Kley, and H. Dette, A new approach for open-end sequential change-point monitoring, J. Time Series Anal. 42 (2021), pp. 63–84.
  • B. Jing, T. Min, and W. Zhou, Transforming the empirical likelihood towards better accuracy, Canad. J. Statist. 45 (2017), pp. 340–352.
  • K. Kim, J.H. Park, M. Lee, and J.W. Song, Unsupervised change point detection and trend prediction for financial time-series using a new cusum-based approach, IEEE. Access. 10 (2022), pp. 34690–34705.
  • G. Lorden, Procedures for reacting to a change in distribution, Ann. Math. Statist. 42 (1971), pp. 1897–1908.
  • G. Lorden and M. Pollak, Average run length to false alarm for surveillance schemes designed with partially specified pre-change distribution, Ann. Statist. 25 (1997), pp. 1284–1310.
  • Y. Mei, Sequential change-point detection when unknown parameters are present in the pre-change distribution, Ann. Statist. 34 (2006), pp. 92–122.
  • G. Moustakides, Optimal stopping times for detecting changes in distributions, Ann. Statist. 14 (1986), pp. 1379–1387.
  • G. Moustakides, A. Polunchenko, and A. Tartakovsky, Numerical comparison of cusum and Shiryaev-Roberts procedures for detecting changes in distributions, Comm. Statist. Theory Methods 38 (2009), pp. 3225–3239.
  • A. Owen, Empirical likelihood ratio confidence intervals for a single functional, Biometrika 75 (1988), pp. 237–249.
  • A. Owen, Empirical likelihood ratio confidence regions, Ann. Statist. 18 (1990), pp. 90–120.
  • E. Page, Continuous inspection schemes, Biometrika 41 (1954), pp. 100–115.
  • M. Pollak, Optimal detection of a change in distribution, Ann. Statist. 13 (1985), pp. 206–227.
  • M. Pollak and A. Tartakovsky, Optimality properties of the Shiryaev-Roberts procedure, Statist. Sinica 19 (2009), pp. 1729–1739.
  • A. Polunchenko and A. Tartakovsky, On optimality of the Shiryaev-Roberts procedure for detecting a change in distribution, Ann. Statist. 38 (2010), pp. 3445–3457.
  • F. Proschan, Theoretical explanation of observed decreasing failure rate, Technometrics 42 (2000), pp. 7–11.
  • J. Qin and J. Lawless, Empirical likelihood and general estimating equations, Ann. Statist. 22 (1994), pp. 300–325.
  • S. Ratnasingam and W. Ning, Sequential change point detection for high-dimensional data using nonconvex penalized quantile regression, Biom. J. 63 (2021), pp. 575–598.
  • S. Roberts, A comparison of some control chart procedures, Technometrics 8 (1966), pp. 411–430.
  • A. Shiryaev, On optimum methods in quickest detection problems, Theory Probab. Appl. 8 (1963), pp. 22–46.
  • P. Stewart and W. Ning, Modified empirical likelihood-based confidence intervals for data containing many zero observations, Comput. Statist. 35 (2020), pp. 2019–2042.
  • A. Tartakovsky, On asymptotic optimality in sequential changepoint detection: Non-iid case, IEEE Trans. Inform. Theory 63 (2017), pp. 3433–3450.
  • G. Verdier, An empirical likelihood-based cusum for on-line model change detection, Comm. Statist. Theory Methods 49 (2020), pp. 1818–1839.
  • Y. Wu, A combined SR-CUSUM procedure for detecting common changes in panel data, Comm. Statist. Theory Methods 48 (2018), pp. 4302–4319.
  • W. Zhang and Y. Mei, Bandit change-point detection for real-time monitoring high-dimensional data under sampling control, Technometrics 65 (2023), pp. 33–43.

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