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Department of Statistics

Research

Group

 

  1. Fan Wang, Gengyu Xue, Alexander Kent, PhD students.

  2. Mengchu Li, former postdoc and former PhD student, currently postdoc at the University of Warwick.

  3. Haotian Xu, former postdoc, currently Harrison Assistant Professor at the University of Warwick.

 

Most of my change point analysis papers have methods implemented in the R package changepoints. This package is mainly written by Haotian Xu.

 

Preprints

 

  1. Federated Transfer Learning with Differential Privacy. (2024) arXiv preprint. [pdf]

    Mengchu Li, Ye Tian, Yang Feng and Y.

  2. Transfer learning for piecewise-constant mean estimation: Optimality, ell_1- and ell_0-penalisation. (2023) arXiv preprint. [pdf]

    Fan Wang and Y.

  3. Multilayer random dot product graphs: Estimation and online change point detection. (2023) arXiv preprint. [pdf]

    Fan Wang, Wanshan Li, Oscar Hernan Madrid Padilla, Y. and Alessandro Rinaldo

  4. Robust mean change point testing in high-dimensional data with heavy tails. (2023) arXiv preprint. [pdf]

    Mengchu Li, Yudong Chen, Tengyao Wang and Y.

  5. High-Dimensional Dynamic Pricing under Non-Stationarity: Learning and Earning with Change-Point Detection. (2023) arXiv preprint. [pdf]

    Zifeng Zhao, Feiyu Jiang, Y. and Xi Chen

  6. Dynamic and heterogeneous treatment effects with abrupt changes. (2022) arXiv preprint. [pdf]

    Oscar Hernan Madrid Padilla and Y.

  7. Online network change point detection with missing values. (2021) arXiv preprint. [pdf]

    Paromita Dubey, Haotian Xu and Y.

  8. Generalized non-stationary bandits. (2021) arXiv preprint. [pdf]

    Anne Gael Manegueu, Alexandra Carpentier and Y.

  9. A review on minimax rates in change point detection and localisation. (2020) arXiv preprint. [pdf]

    Y.

  10. Localizing changes in high-dimensional vector autoregressive processes. (2019) arXiv preprint. [pdf]

    Daren Wang, Y., Alessandro Rinaldo and Rebecca Willett

 

Publications

 

  1. Quickest Detection in High-Dimensional Linear Regression Models via Implicit Regularization. (2024) IEEE International Symposium on Information Theory.

    Qunzhi Xu, Y. and Yajun Mei.

  2. Change point inference in high-dimensional regression models under temporal dependence. (2024) Annals of Statistics, to appear. [pdf]

    Haotian Xu, Daren Wang, Zifeng Zhao and Y.

  3. Optimal network online change point localisation. (2023) SIAM Journal on Mathematics of Data Science, to appear. [pdf]

    Y., Oscar Hernan Madrid Padilla, Daren Wang and Alessandro Rinaldo

  4. A Note on Online Change Point Detection. (2023) Sequential Analysis, to appear. DOI: 10.1080/07474946.2023.2276170. [pdf]

    Y., Oscar Hernan Madrid Padilla, Daren Wang and Alessandro Rinaldo

  5. Change point detection and inference in multivariable nonparametric models under mixing conditions. (2023) NeurIPS. [pdf]

    Carlos Misael Madrid Padilla, Haotian Xu, Daren Wang, Oscar Hernan Madrid Padilla and Y.

  6. On robustness and local differential privacy. (2023) Annals of Statistics, Vol. 51, No. 2, 717-737. [pdf]

    Mengchu Li, Thomas B. Berrett and Y.

  7. Change-point Detection for Sparse and Dense Functional Data in General Dimensions. (2022) NeurIPS. [pdf]

    Carlos Misael Madrid Padilla, Daren Wang, Zifeng Zhao and Y.

  8. Network change point localisation under local differential privacy. (2022) NeurIPS. [pdf]

    Mengchu Li, Thomas B. Berrett and Y.

  9. Change point localization in dependent dynamic nonparametric random dot product graphs. (2022) Journal of Machine Learning Research, 23(234), 1-59. [pdf]

    Oscar Hernan Madrid Padilla, Y. and Carey E. Priebe

  10. Functional Linear Regression with Mixed Predictors. (2022) Journal of Machine Learning Research, 3(266), 1-94 [pdf]

    Daren Wang, Zifeng Zhao, Y., and Rebecca Willett

  11. Detecting abrupt changes in high-dimensional self-exciting Poisson processes. (2022) Statistica Sinica, doi: 10.5705/ss.202021.0221. [pdf]

    Daren Wang, Y. and Rebecca Willett

  12. Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients. (2022) In International Conference on Artificial Intelligence and Statistics, pp. 4309–4338. PMLR. (Oral presentation). [pdf]

    Fan Wang, Oscar Hernan Madrid Padilla, Y. and Alessandro Rinaldo

  13. Optimal partition recovery in general graphs. (2022) In International Conference on Artificial Intelligence and Statistics, pp. 4339–4358. PMLR. [pdf]

    Y., Oscar Hernan Madrid Padilla and Alessandro Rinaldo

  14. Graph matching beyond perfectly-overlapping Erd\H{o}s--R\'enyi random graphs. (2021) Statistics and Computing, Vol. 32, No. 1, pp. 1–16. [pdf]

    Yaofang Hu, Wanjie Wang and Y.

  15. Localising change points in piecewise polynomials of general degrees. (2021) Electronic Journal of Statistics, Vol. 16, No. 1, pp. 1855–1890. [pdf]

    Y., Sabyasachi Chatterjee and Haotian Xu

  16. Optimal nonparametric multivariate change point detection and localization. (2021) IEEE Transactions on Information Theory, Vol. 68, No. 3, pp. 1922–1944. [pdf]

    Oscar Hernan Madrid Padilla, Y., Daren Wang and Alessandro Rinaldo

  17. Lattice partition recovery with dyadic CART. (2021) Advances in Neural Information Processing Systems, 34, 26143–26155. [pdf]

    Oscar Hernan Madrid Padilla, Y. and Alessandro Rinaldo

  18. Locally private online change point detection. (2021) Advances in Neural Information Processing Systems, 34, 3425-3437. [pdf]

    Thomas B. Berrett and Y.

  19. Adversarially Robust Change Point Detection. (2021) Advances in Neural Information Processing Systems, 34, 22955–22967. [pdf]

    Mengchu Li and Y.

  20. Optimal nonparametric change point detection and localization. (2021) Electronic Journal of Statistics, Vol. 15, No. 1, 1154-1201. [pdf]

    Oscar Hernan Madrid Padilla, Y., Daren Wang and Alessandro Rinaldo

  21. Localizing changes in high-dimensional regression models. (2021) In International Conference on Artificial Intelligence and Statistics, pp. 2089–2097. PMLR. [pdf]

    Alessandro Rinaldo, Daren Wang, Qin Wen, Rebecca Willett and Y.

  22. Optimal Covariance Change Point Detection in High Dimension. (2020) Bernoulli, Vol. 27, No. 1, pp. 554–575. [pdf]

    Daren Wang, Y. and Alessandro Rinaldo

  23. Univariate mean change point detection: penalization, CUSUM and optimality. (2020) Electronic Journal of Statistics, Vol. 14, No. 1, 1917--1961. [pdf]

    Daren Wang, Y. and Alessandro Rinaldo

  24. Event history analysis of dynamic networks. (2020) Biometrika, Vol. 108, No. 1, pp. 223–230. [pdf]

    Tony Sit, Zhiliang Ying and Y.

  25. Optimal change point detection and localization in sparse dynamic networks. (2021) Annals of Statistics, Vol. 49, No. 1, 203-232. [pdf]

    Daren Wang, Y. and Alessandro Rinaldo

  26. Spectral analysis of high-dimensional time series. (2019) Electronic Journal of Statistics, Vol. 13, 4079-4101. [pdf]

    Mark Fiecas, Chenlei Leng, Weidong Liu and Y.

  27. Confidence intervals for high-dimensional Cox models. (2021) Statistica Sinica, 31, 243--267. [pdf]

    Y., Jelena Bradic and Richard J. Samworth

  28. Two new approaches for the visualisation of models for network meta-analysis. (2019) BMC Medical Research Methodology, Vol. 19, No. 1, pp. 1–18. [pdf]

    Martin Law, Navid Alam, Areti Angeliki Veroniki, Y. and Dan Jackson

  29. Link prediction for inter-disciplinary collaboration via co-authorship network. (2018) Social Network Analysis and Mining, 8, 25. [pdf]

    Haeran Cho and Y.

  30. The restricted consistency property of leave-nv-out cross-validation for high-dimensional variable selection. (2019) Statistica Sinica, 29, 1607--1630 [pdf]

    Yang Feng and Y.

  31. Estimating whole brain dynamics using spectral clustering. (2017) Journal of Royal Statistical Society, Series C, 66, 607--627. [pdf]

    Ivor Cribben and Y.

  32. How many communities are there? (2017) Journal of Computational and Graphical Statistics, 26, 171--181. [pdf]

    Diego Franco Saldana, Y. and Yang Feng

  33. A useful variant of the Davis--Kahan theorem for statisticians. (2015) Biometrika, 102, 315--323. [pdf]

    Y., Tengyao Wang and Richard J. Samworth

  34. Modified cross-validation for penalized high-dimensional linear regression models. (2014) Journal of Computational and Graphical Statistics, 23, 1009--1027. [pdf]

    Y. and Yang Feng

  35. Apple: Approximate Path for Penalized Likelihood Estimators (2013). Statistics and Computing, 24, 803--819 [pdf]

    Y. and Yang Feng

  36. Oracle inequalities for the Lasso in the Cox model (2013). The Annals of Statistics, 41, 1142--1165. [pdf]

    Jian Huang, Tingni Sun, Zhiliang Ying, Y. and Cun-Hui Zhang

 

Invited discussions

 

  1. Discussion of “Should we sample a time series more frequently? Decision support via multirate spectrum estimation” by Nason, Powell, Elliott and Smith. (2017) Journal of the Royal Statistical Society, Series A. 180, 384--385[pdf]

    Y. and Ivor Cribben

  2. Invited discussion of Large covariance estimation by thresholding principal orthogonal complements by Fan et al (2013). Journal of Royal Statistical Society, Series B, 75, 656--658. [pdf]

    Y. and Richard J. Samworth

 

Software

 

  1. GMPro: Graph Matching with Degree Profiles (2020). Available from cran.

  2. fcd, an R package for fused community detection (2013). Available from cran.

  3. APPLE, an R package for Approximate Path for Penalized Likelihood Estimators (2012). Available from cran.

 

Ph.D. thesis

 

  1. Contributions to high-dimensional variable selection. Fudan University, June 2013.

    Supervisor: Professor Zhiliang Ying.