Yi Yu  于怡




If you are also a statistician, you may have not cited my papers yet, but you must have cited my name tons of times, when you need an observation Yi, i = 1, ..., n. :)

I am a Research Associate in the Statistical Laboratory, University of Cambridge, and also a Lecturer in Statistical Sciences (on leave till 1 Jan 2017) in the School of Mathematics, University of Bristol. 


I am currently co-organising two invited only workshops: Statistical Network and High-Dimensional Data Analysis: Theory and Applications and Alan Turing Institute Scoping Workshop -- Statistical and Computational Challenges in Large-Scale Data Analysis


My research interests are high-dimensional statistics, network studies, survival analysis and applications in brain imaging data.

Working papers

  1. Recurrent event survival analysis in dynamic network data

      Tony Sit, Zhiliang Ying and Y.

  2. Fused community detection.

      Yang Feng, Richard J. Samworth and Y.

  3. Confidence Intervals for High-Dimensional Cox Models.

      Jelena Bradic, Richard J. Samworth and Y.

Publications and preprints

  1. Estimating whole brain dynamics using spectral clustering. (2015) arXiv preprint. [pdf]

      Ivor Cribben and Y.

  2. Consistent cross-validation for tuning parameter selection in high-dimensional variable selection. (2013) arXiv preprint. [pdf, R codes]

      Yang Feng and Y.

  1. How many communities are there? (2015) Journal of Computational and Graphical Statistics, to appear. [pdf]

      Diego Franco Saldana, Y. and Yang Feng

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

      Y., Tengyao Wang and Richard J. Samworth

  3. Modified cross-validation for penalized high-dimensional linear regression models. (2013) Journal of Computational and Graphical Statistics, to appear. [pdf, R codes]

      Y. and Yang Feng

  4. Invited discussion of Large covariance estimation by thresholding principal orthogonal complements by Fan et al (2013). Journal of Royal Statistical Society, Series B, Vol. 75, Part 4, 656-658. [pdf]

      Y. and Richard J. Samworth

  5. Apple: Approximate Path for Penalized Likelihood Estimators (2013). Statistics and Computing, Vol. 24, Issue 5, 803-819 [pdf]

      Y. and Yang Feng

  6. Oracle inequalities for the Lasso in the Cox model (2013). The Annals of Statistics, Vol. 41, No. 3, 1142-1165. [pdf]

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


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



  1. NCP, 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.


Room D2.04

Statistical Laboratory, Centre for Mathematical Sciences

Wilberforce Road, Cambridge, CB30WB.

Email: y.yu@statslab.cam.ac.uk

Phone: +44 (0)1223 337949