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Research Article

A distribution-free method for change point detection in non-sparse high dimensional data

ORCID Icon & ORCID Icon
Received 05 Aug 2023, Accepted 30 May 2024, Accepted author version posted online: 12 Jun 2024
Accepted author version

References

  • Barigozzi, M., Cho, H. and Fryzlewicz, P. (2018), ‘Simultaneous multiple change-point and factor analysis for high-dimensional time series’, Journal of Econometrics 206, 187–225.
  • Chen, H. and Zhang, N. (2015), ‘Graph-based change-point detection’, The Annals of Statistics 43, 139–176.
  • Chu, L. and Chen, H. (2019), ‘Asymptotic distribution-free change-point detection for multivariate and non-euclidean data’, The Annals of Statistics 47, 382–414.
  • Enikeeva, F. and Harchaoui, Z. (2019), ‘High-dimensional change-point detection under sparse alternatives’, The Annals of Statistics 47, 2051–2079.
  • Follain, B., Wang, T. and Samworth, R. J. (2022), ‘High-dimensional changepoint estimation with heterogeneous missingness’, Journal of the Royal Statistical Society Series B 84, 1023–1055.
  • Fryzlewicz, P. (2014), ‘Wild binary segmentation for multiple change-point detection’, The Annals of Statistics 42, 2243–2281.
  • Garreau, D. and Arlot, S. (2018), ‘Consistent change-point detection with kernels’, Electronic Journal of Statistics 12, 4440–4486.
  • Grundy, T., Killick, R. and Mihaylov, G. (2020), ‘High-dimensional changepoint detection via a geometrically inspired mapping’, Statistics and Computing 30, 1155–1166.
  • Hahn, G., Fearnhead, P. and Eckley, I. A. (2020), ‘BayesProject: Fast computation of a projection direction for multivariate changepoint detection’, Statistics and Computing 30, 1691–1705.
  • Hall, P., Marron, J. S. and Neeman, A. (2005), ‘Geometric representation of high dimension, low sample size data’, Journal of the Royal Statistical Society: Series B 67, 427–444.
  • Jung, S. and Marron, J. (2009), ‘PCA consistency in high dimension, low sample size context’, The Annals of Statistics 37, 4104–4130.
  • Lee, S., Seo, M. H. and Shin, Y. (2016), ‘The lasso for high dimensional regression with a possible change point’, Journal of the Royal Statistical Society: Series B 78, 193–210.
  • Li, J. (2020), ‘Asymptotic distribution-free change-point detection based on interpoint distances for high-dimensional data’, Journal of Nonparametric Statistics 32, 157–184.
  • Li, J., Xu, M., Zhong, P.-S. and Li, L. (2019), ‘Change point detection in the mean of high-dimensional time series data under dependence’. arXiv:1903.07006.
  • Liu, B., Zhang, X. and Liu, Y. (2022), ‘High dimensional change point inference: Recent developments and extensions’, Journal of Multivariate Analysis 188, 1–19.
  • Liu, B., Zhou, C., Zhang, X. and Liu, Y. (2020), ‘A unified data-adaptive framework for high dimensional change point detection’, Journal of the Royal Statistical Society: Series B 82, 933–963.
  • Matteson, D. S. and James, N. A. (2014), ‘A nonparametric approach for multiple change point analysis of multivariate data’, Journal of the American Statistical Association 109, 334–345.
  • Safikhani, A. and Shojaie, A. (2022), ‘Joint structural break detection and parameter estimation in high-dimensional nonstationary VAR models’, Journal of the American Statistical Association 117, 251–264.
  • Statista Research Department (2022), ‘Weekly development of the S&P 500 index from January 2020 to September 2022’, https://www.statista.com/statistics/1104270/weekly-sandp-500-index-performance/.
  • Utev, S. A. (1990), ‘Central limit theorem for dependent random variables’, Probability Theory and Mathematical Statistics 2, 519–528.
  • Wang, T. and Samworth, R. J. (2018), ‘High dimensional change point estimation via sparse projection’, Journal of the Royal Statistical Society: Series B 80, 57–83.
  • Xiao, W., Huang, X., He, F., Silva, J., Emrani, S. and Chaudhuri, A. (2019), ‘Online robust principal component analysis with change point detection’, IEEE Transactions on Multimedia 22, 59–68.
  • Yu, M. and Chen, X. (2021), ‘Finite sample change point inference and identification for high-dimensional mean vectors’, Journal of the Royal Statistical Society: Series B 83, 247–270.
  • Zhang, L. and Drikvandi, R. (2023), ‘High dimensional change points: challenges and some proposals’, Proceedings of the 5th International Conference on Statistics: Theory and Applications, Paper No. 142. DOI: 10.11159/icsta23.142.