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A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data

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Cited by:

  1. DAVID E. ALLEN & MICHAEL McALEER & ROBERT J. POWELL & ABHAY K. SINGH, 2018. "Non-Parametric Multiple Change Point Analysis Of The Global Financial Crisis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-23, June.
  2. Jong-Min Kim & Ning Wang & Yumin Liu, 2020. "Multi-Stage Change Point Detection with Copula Conditional Distribution with PCA and Functional PCA," Mathematics, MDPI, vol. 8(10), pages 1-23, October.
  3. Michael Messer & Stefan Albert & Gaby Schneider, 2018. "The multiple filter test for change point detection in time series," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 589-607, August.
  4. Fernando F. Ferreira & A. Christian Silva & Ju-Yi Yen, 2019. "Detailed study of a moving average trading rule," Papers 1907.00212, arXiv.org.
  5. Li, Yuzhao & Liu, Yong & Zhao, Lei & Hastings, Alan & Guo, Huaicheng, 2015. "Exploring change of internal nutrients cycling in a shallow lake: A dynamic nutrient driven phytoplankton model," Ecological Modelling, Elsevier, vol. 313(C), pages 137-148.
  6. 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).
  7. Brault, Vincent & Ouadah, Sarah & Sansonnet, Laure & Lévy-Leduc, Céline, 2018. "Nonparametric multiple change-point estimation for analyzing large Hi-C data matrices," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 143-165.
  8. Kleiber, Christian, 2016. "Structural Change in (Economic) Time Series," Working papers 2016/06, Faculty of Business and Economics - University of Basel.
  9. Ping‐Shou Zhong, 2023. "Homogeneity tests of covariance for high‐dimensional functional data with applications to event segmentation," Biometrics, The International Biometric Society, vol. 79(4), pages 3332-3344, December.
  10. Mintaek Lee & Jaechoul Lee, 2021. "Long‐term trend analysis of extreme coastal sea levels with changepoint detection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 434-458, March.
  11. Baoni Li & Lihua Xiong & Quan Zhang & Shilei Chen & Han Yang & Shuhui Guo, 2022. "Effects of land use/cover change on atmospheric humidity in three urban agglomerations in the Yangtze River Economic Belt, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 577-613, August.
  12. Ariyarathne, Sakitha & Gangammanavar, Harsha & Sundararajan, Raanju R., 2022. "Change point detection-based simulation of nonstationary sub-hourly wind time series," Applied Energy, Elsevier, vol. 310(C).
  13. Jurgita Markevičiūtė & Jolita Bernatavičienė & Rūta Levulienė & Viktor Medvedev & Povilas Treigys & Julius Venskus, 2022. "Impact of COVID-19-Related Lockdown Measures on Economic and Social Outcomes in Lithuania," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
  14. Zacharia Issa & Blanka Horvath, 2023. "Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures," Papers 2306.15835, arXiv.org.
  15. Zifeng Zhao & Feiyu Jiang & Xiaofeng Shao, 2022. "Segmenting time series via self‐normalisation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1699-1725, November.
  16. Wen‐Yu Hua & Debashis Ghosh, 2015. "Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies," Biometrics, The International Biometric Society, vol. 71(3), pages 812-820, September.
  17. James, Nicholas A. & Matteson, David S., 2015. "ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i07).
  18. Dai, Xingyu & Xiao, Ling & Wang, Qunwei & Dhesi, Gurjeet, 2021. "Multiscale interplay of higher-order moments between the carbon and energy markets during Phase III of the EU ETS," Energy Policy, Elsevier, vol. 156(C).
  19. Shi, Xuesheng & Gallagher, Colin & Lund, Robert & Killick, Rebecca, 2022. "A comparison of single and multiple changepoint techniques for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
  20. J. S. Allison & M. Hušková & S. G. Meintanis, 2018. "Testing the adequacy of semiparametric transformation models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 70-94, March.
  21. Chen, Feifei & Meintanis, Simos G. & Zhu, Lixing, 2019. "On some characterizations and multidimensional criteria for testing homogeneity, symmetry and independence," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 125-144.
  22. Holger Dette & Theresa Eckle & Mathias Vetter, 2020. "Multiscale change point detection for dependent data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1243-1274, December.
  23. Stefan Albert & Michael Messer & Julia Schiemann & Jochen Roeper & Gaby Schneider, 2017. "Multi-Scale Detection of Variance Changes in Renewal Processes in the Presence of Rate Change Points," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 1028-1052, November.
  24. José M. Oller & Albert Satorra & Adolf Tobeña, 2019. "Unveiling pathways for the fissure among secessionists and unionists in Catalonia: identities, family language, and media influence," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-13, December.
  25. Michael Messer, 2022. "Bivariate change point detection: Joint detection of changes in expectation and variance," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 886-916, June.
  26. Neil Hwang & Jiarui Xu & Shirshendu Chatterjee & Sharmodeep Bhattacharyya, 2022. "The Bethe Hessian and Information Theoretic Approaches for Online Change-Point Detection in Network Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 283-320, June.
  27. Max Wornowizki & Roland Fried & Simos G. Meintanis, 2017. "Fourier methods for analyzing piecewise constant volatilities," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(3), pages 289-308, July.
  28. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
  29. Nick James, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Papers 2101.00576, arXiv.org, revised Feb 2021.
  30. Bin Liu & Cheng Zhou & Xinsheng Zhang & Yufeng Liu, 2020. "A unified data‐adaptive framework for high dimensional change point detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 933-963, September.
  31. Florian Pein & Hannes Sieling & Axel Munk, 2017. "Heterogeneous change point inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1207-1227, September.
  32. Bergamelli, Michele & Bianchi, Annamaria & Khalaf, Lynda & Urga, Giovanni, 2019. "Combining p-values to test for multiple structural breaks in cointegrated regressions," Journal of Econometrics, Elsevier, vol. 211(2), pages 461-482.
  33. James, Nick, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
  34. Liu, Bin & Zhang, Xinsheng & Liu, Yufeng, 2022. "High dimensional change point inference: Recent developments and extensions," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
  35. Fryzlewicz, Piotr, 2020. "Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection," LSE Research Online Documents on Economics 103430, London School of Economics and Political Science, LSE Library.
  36. Hajra Siddiqa & Sajid Ali & Ismail Shah, 2021. "Most recent changepoint detection in censored panel data," Computational Statistics, Springer, vol. 36(1), pages 515-540, March.
  37. Michael Messer & Gaby Schneider, 2017. "The shark fin function: asymptotic behavior of the filtered derivative for point processes in case of change points," Statistical Inference for Stochastic Processes, Springer, vol. 20(2), pages 253-272, July.
  38. Han Lin Shang & Ruofan Xu, 2022. "Change point detection for COVID-19 excess deaths in Belgium," Journal of Population Research, Springer, vol. 39(4), pages 557-565, December.
  39. Celisse, A. & Marot, G. & Pierre-Jean, M. & Rigaill, G.J., 2018. "New efficient algorithms for multiple change-point detection with reproducing kernels," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 200-220.
  40. 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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