MoChA
Modern Change point Analysis
working group
Modern Change point Analysis
working group
We are a productive group, actively working on various aspects of modern change point analysis. On this website, you can find a list of our works and recent activities. You can see who we are here.
R package: changepoints: A Collection of Change-Point Detection Methods
Xu, H., Wang, D., Zhao, Z., & Yu, Y. (2022). Change point inference in high-dimensional regression models under temporal dependence. arXiv preprint arXiv:2207.12453.
Padilla, O. H. M., & Yu, Y. (2022). Dynamic and heterogeneous treatment effects with abrupt changes. arXiv preprint arXiv:2206.09092.
Padilla, C. M. M., Wang, D., Zhao, Z., & Yu, Y. (2022). Change-point detection for sparse and dense functional data in general directions. arXiv preprint arXiv:2205.09252. [link]
Li, M., Berrett, T. B., & Yu, Y. (2022). Network change point localisation under local differential privacy. arXiv preprint arXiv:2205.07144. [link]
Shin, J., Ramdas, A., & Rinaldo, A. (2022). E-detectors: a nonparametric framework for online changepoint detection. arXiv preprint arXiv:2203.03532. [link]
Dubey, P., Xu, H., & Yu, Y. (2021). Online network change point detection with missing values. arXiv preprint arXiv:2110.06450. [link]
Cappello, L., Padilla, O. H. M., & Palacios, J. A. (2021). Scalable Bayesian change point detection with spike and slab priors. arXiv preprint arXiv:2106.10383. [link]
Yu, Y., Padilla, O. H. M., Wang, D., & Rinaldo, A. (2021). Optimal network online change point localisation. arXiv preprint arXiv:2101.05477. [link]
Padilla, O. H. M., Yu, Y., & Priebe, C. E. (2019). Change point localization in dependent dynamic nonparametric random dot product graphs. arXiv preprint arXiv:1911.07494. [link]
Wang, D., Yu, Y., Rinaldo, A., & Willett, R. (2019). Localizing changes in high-dimensional vector autoregressive processes. arXiv preprint arXiv:1909.06359. [link]
Wang, D., Yu, Y., & Willett, R. (2022). Detecting abrupt changes in high-dimensional self-exciting poisson processes. Statistica Sinica, to appear. [link]
Wang, F., Padilla, O. H. M., Yu, Y., & Rinaldo, A. (2022). Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients. AISTATS (oral). [link]
Yu, Y., Padilla, O. H. M., & Rinaldo, A. (2022). Optimal partition recovery in general graphs. AISTATS. [link]
Yu, Y., Chatterjee, S., & Xu, H. (2022). Localising change points in piecewise polynomials of general degrees. Electronic Journal of Statistics, 16(1), 1855-1890. [link]
Shin, J., Ramdas, A., & Rinaldo, A. (2021). Nonparametric iterated-logarithm extensions of the sequential generalized likelihood ratio test. IEEE Journal on Selected Areas in Information Theory, 2(2), 691-704. [link]
Padilla, O. H. M., Yu, Y., Wang, D., & Rinaldo, A. (2021). Optimal nonparametric multivariate change point detection and localization. IEEE Transactions on Information Theory. [link]
Madrid Padilla, O. H., Yu, Y., & Rinaldo, A. (2021). Lattice partition recovery with dyadic CART. Advances in Neural Information Processing Systems, 34. [link]
Berrett, T., & Yu, Y. (2021). Locally private online change point detection. Advances in Neural Information Processing Systems, 34. [link]
Li, M., & Yu, Y. (2021). Adversarially robust change point detection. Advances in Neural Information Processing Systems, 34. [link]
Padilla, O. H. M., Yu, Y., Wang, D., & Rinaldo, A. (2021). Optimal nonparametric change point analysis. Electronic Journal of Statistics, 15(1), 1154-1201. [link]
Rinaldo, A., Wang, D., Wen, Q., Willett, R., & Yu, Y. (2021, March). Localizing changes in high-dimensional regression models. In International Conference on Artificial Intelligence and Statistics (pp. 2089-2097). PMLR. [link]
Wang, D., Yu, Y., & Rinaldo, A. (2021). Optimal covariance change point localization in high dimensions. Bernoulli, 27(1), 554-575. [link]
Wang, D., Yu, Y., & Rinaldo, A. (2021). Optimal change point detection and localization in sparse dynamic networks. The Annals of Statistics, 49(1), 203-232. [link]
Wang, D., Yu, Y., & Rinaldo, A. (2020). Univariate mean change point detection: Penalization, cusum and optimality. Electronic Journal of Statistics, 14(1), 1917-1961. [link]
Structural breaks and shape constraints, ICMS workshop, 16-20 May 2022, Edinburgh. [link]
MoChA workshop, late May 2023, University of Warwick.
NSF DMS 2015489
EPSRC EP/V013432/1