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Extensions of some classical methods in change point analysis

Citations

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Cited by:

  1. Sergio Alvarez-Andrade & Salim Bouzebda & Aimé Lachal, 2018. "Strong approximations for the p-fold integrated empirical process with applications to statistical tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 826-849, December.
  2. Haeran Cho & Claudia Kirch, 2022. "Two-stage data segmentation permitting multiscale change points, heavy tails and dependence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 653-684, August.
  3. Lee, Sangyeol & Meintanis, Simos G. & Pretorius, Charl, 2022. "Monitoring procedures for strict stationarity based on the multivariate characteristic function," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  4. Saisai Ding & Xiaoqin Li & Xiang Dong & Wenzhi Yang, 2020. "The Consistency of the CUSUM-Type Estimator of the Change-Point and Its Application," Mathematics, MDPI, vol. 8(12), pages 1-12, November.
  5. Bouzebda, Salim & Ferfache, Anouar Abdeldjaoued, 2023. "Asymptotic properties of semiparametric M-estimators with multiple change points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
  6. Kleiber, Christian, 2016. "Structural Change in (Economic) Time Series," Working papers 2016/06, Faculty of Business and Economics - University of Basel.
  7. Jiang, Feiyu & Wang, Runmin & Shao, Xiaofeng, 2023. "Robust inference for change points in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
  8. Jaromír Antoch & Jan Hanousek & Lajos Horváth & Marie Hušková & Shixuan Wang, 2019. "Structural breaks in panel data: Large number of panels and short length time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 828-855, August.
  9. Fabrizio Ghezzi & Eduardo Rossi & Lorenzo Trapani, 2024. "Fast Online Changepoint Detection," Papers 2402.04433, arXiv.org.
  10. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2022. "Change point analysis of covariance functions: A weighted cumulative sum approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  11. Marco Barassi & Lajos Horváth & Yuqian Zhao, 2020. "Change‐Point Detection in the Conditional Correlation Structure of Multivariate Volatility Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 340-349, April.
  12. Horváth, Lajos & Liu, Zhenya & Rice, Gregory & Wang, Shixuan, 2020. "Sequential monitoring for changes from stationarity to mild non-stationarity," Journal of Econometrics, Elsevier, vol. 215(1), pages 209-238.
  13. Yudong Chen & Tengyao Wang & Richard J. Samworth, 2022. "High‐dimensional, multiscale online changepoint detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 234-266, February.
  14. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
  15. Daniela Jarušková, 2015. "Detecting non-simultaneous changes in means of vectors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 681-700, December.
  16. Federico A. Bugni & Jia Li & Qiyuan Li, 2023. "Permutation‐based tests for discontinuities in event studies," Quantitative Economics, Econometric Society, vol. 14(1), pages 37-70, January.
  17. Claudia Kirch & Christina Stoehr, 2022. "Sequential change point tests based on U‐statistics," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1184-1214, September.
  18. Bertille Follain & Tengyao Wang & Richard J. Samworth, 2022. "High‐dimensional changepoint estimation with heterogeneous missingness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 1023-1055, July.
  19. Tianming Xu & Yuesong Wei, 2023. "Ratio Test for Mean Changes in Time Series with Heavy-Tailed AR( p ) Noise Based on Multiple Sampling Methods," Mathematics, MDPI, vol. 11(18), pages 1-14, September.
  20. Follain, Bertille & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional changepoint estimation with heterogeneous missingness," LSE Research Online Documents on Economics 115014, London School of Economics and Political Science, LSE Library.
  21. Chen, Yudong & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional, multiscale online changepoint detection," LSE Research Online Documents on Economics 113665, London School of Economics and Political Science, LSE Library.
  22. Lorenzo Escot & Julio E. Sandubete & Łukasz Pietrych, 2023. "Detecting Structural Changes in Time Series by Using the BDS Test Recursively: An Application to COVID-19 Effects on International Stock Markets," Mathematics, MDPI, vol. 11(23), pages 1-18, December.
  23. Ricardo C. Pedroso & Rosangela H. Loschi & Fernando Andrés Quintana, 2023. "Multipartition model for multiple change point identification," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 759-783, June.
  24. Hahn, Georg, 2022. "Online multivariate changepoint detection with type I error control and constant time/memory updates per series," Statistics & Probability Letters, Elsevier, vol. 181(C).
  25. Liu, Bin & Zhang, Xinsheng & Liu, Yufeng, 2022. "High dimensional change point inference: Recent developments and extensions," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
  26. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2023. "Testing for changes in linear models using weighted residuals," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
  27. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2020. "Change-point methods for multivariate time-series: paired vectorial observations," Statistical Papers, Springer, vol. 61(4), pages 1351-1383, August.
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