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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.

yi.yu.2@warwick.ac.uk

+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


Notes


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

    Y.


Software


  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.