Skip to main content Skip to navigation

Dr Yi Yu 于怡


Yi Yu 于怡



I am an Associate Professor in the Department of Statistics, University of Warwick and a Turing Fellow at the Alan Turing Institute, previously a Lecturer in the University of Bristol, a postdoc of Professor Richard Samworth and a graduate student of Professor Zhiliang Ying. I obtained my academic degrees from Fudan University (B.S. in Mathematics, July 2009 and Ph.D. in Mathematical Statistics, July 2013).

My research interests are high-dimensional statistics and network studies. I have been working on theoretical, methodological and computational aspects of variable selection, post-selection inference and tuning parameter selection in regression problems, survival analysis, network analysis, and time series analysis.

Room 4.11

Department of Statistics, University of Warwick

Coventry, CV4 7AL, United Kingdom.

+44 (0)24761 50134

Office hour: 3.30-4.30pm on Tuesdays and Wednesdays (starting from the week of 7 Oct 2019).

I will be teaching Financial Time Series and Medical Statistics in 2019-20.

Editorial Service

  1. Associate Editor, Biometrika

Publications and preprints

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

    Daren Wang, Y., Alessandro Rinaldo and Rebecca Willett

  2. Optimal nonparametric change point detection and localization. (2019) arXiv preprint. [pdf]

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

  3. Univariate mean change point detection: penalization, CUSUM and optimality. (2018) arXiv preprint. [pdf]

    Daren Wang, Y. and Alessandro Rinaldo

  4. Event history analysis of dynamic communication networks. (2018) arXiv preprint. [pdf]

    Tony Sit, Zhiliang Ying and Y.

  5. Optimal change point detection and localization in sparse dynamic networks. (2018) arXiv preprint. [pdf]

    Daren Wang, Y. and Alessandro Rinaldo

  6. Optimal Covariance Change Point Detection in High Dimension. (2017) arXiv preprint. [pdf]

    Daren Wang, Y. and Alessandro Rinaldo

  1. 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.

  2. Confidence intervals for high-dimensional Cox models. (2019) Statistica Sinica, to appear. [pdf]

    Y., Jelena Bradic and Richard J. Samworth

  3. Two new approaches for the visualisation of models for network meta-analysis. (2019) BMC Medical Research Methodology, to appear. [pdf]

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

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

    Haeran Cho and Y.

  5. The restricted consistency property of leave-nv-out cross-validation for high-dimensional variable selection . (2018) Statistica Sinica, to appear. [pdf, R codes]

    Yang Feng and Y.

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

    Ivor Cribben and Y.

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

    Diego Franco Saldana, Y. and Yang Feng

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

    Y., Tengyao Wang and Richard J. Samworth

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

    Y. and Yang Feng

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

    Y. and Yang Feng

  11. 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


  1. High-dimensional variable selection in the Cox model. (2010) [pdf]



  1. NCPD, an R package for network change point detection using spectral clustering (2015). [source file, manual]

  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.