I would like to gratefully acknowledge the research support as the Principal Investigator (PI) by the National Science Foundation grants (2014-present, including a CAREER Award), a Simons Fellowship, UIUC Research Board Awards (including an Arnold O. Beckman Award), and a start-up grant from UIUC.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

(* indicates students advised)

  1. Cutoff for exact recovery of Gaussian mixture models.
    Xiaohui Chen, Yun Yang.
    First version: January 2020.
  2. Estimation of dynamic networks for high-dimensional nonstationary time series.
    Mengyu Xu, Xiaohui Chen, Wei Biao Wu.
    Entropy, 22(1):55, 2020. DOI
  3. A robust bootstrap change point test for high-dimensional location parameter.
    Mengjia Yu*, Xiaohui Chen.
    First version: April 2019.
  4. Diffusion K-means clustering on manifolds: provable exact recovery via semidefinite relaxations.
    Xiaohui Chen, Yun Yang.
    First version: March 2019.
  5. Approximating high-dimensional infinite-order U-statistics: statistical and computational guarantees.
    Yanglei Song*, Xiaohui Chen, Kengo Kato.
    Electronic Journal of Statistics, 2019, 13(2), 4794-4848. DOI
  6. U-statistics.
    Xiaohui Chen.
    Wiley StatsRef: Statistics Reference Online, 2019. DOI
  7. Hanson-Wright inequality in Hilbert spaces with application to K-means clustering for non-Euclidean data.
    Xiaohui Chen, Yun Yang.
    First version: October 2018.
  8. Randomized incomplete U-statistics in high dimensions.
    Xiaohui Chen, Kengo Kato.
    Annals of Statistics, 2019, 47(6), 3127-3156. DOI
  9. Finite sample change point inference and identification for high-dimensional mean vectors.
    Mengjia Yu*Xiaohui Chen.
    First version: November 2017. Revised: December 2018.
  10. Jackknife multiplier bootstrap: finite sample approximations to the U-process supremum with applications.
    Xiaohui Chen, Kengo Kato.
    Probability Theory and Related Fields, accepted, 2019+. DOI
  11. Gaussian and bootstrap approximations for high-dimensional U-statistics and their applications.
    Xiaohui Chen.
    Annals of Statistics, 2018, 46(2), 642-678. DOI
    (This paper supersedes the arXiv preprint “Gaussian approximation for the sup-norm of high-dimensional matrix-variate U-statistics and its applications.“)
  12. Inference of high-dimensional linear models with time-varying coefficients.
    Xiaohui Chen, Yifeng He*.
    Statistica Sinica, 2018, 28(1), 255-276. DOI
  13. Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models.
    Xin Ding*, Ziyi Qiu, Xiaohui Chen.
    Electronic Journal of Statistics, 2017, 11(2),3871-3902. DOI
  14. Regularized estimation of linear functionals of precision matrices for high-dimensional time series.
    Xiaohui Chen, Mengyu Xu, Wei Biao Wu.
    IEEE Transactions on Signal Processing, 2016, 64(24), 6459-6470. DOI
  15. Discussion of “High-dimensional autocovariance matrices and optimal linear prediction.”
    Xiaohui Chen.
    Electronic Journal of Statistics, 2015, Vol. 9, 801-810. DOI
  16. A note on moment inequality for quadratic forms.
    Xiaohui Chen.
    Statistics and Probability Letters, 2014, Vol. 92, 83-88. DOI
  17. A genetically-informed, group fMRI connectivity modeling approach: application to Schizophrenia.
    Aiping Liu, Xiaohui Chen, Z. Jane Wang, Qi Xu, Silke Appel-Cresswell, Martin J. McKeown.
    IEEE Transactions on Biomedical Engineering, 2014, 61(3), 946-956. DOI
  18. Covariance and precision matrix estimation for high-dimensional time series.
    Xiaohui Chen, Mengyu Xu, Wei Biao Wu.
    Annals of Statistics, 2013, 41(6), 2994-3021. DOISupplementary file.
  19. Efficient minimax estimation of a class of high-dimensional sparse precision matrices.
    Xiaohui Chen, Young-Heon Kim, Z. Jane Wang.
    IEEE Transactions on Signal Processing, 
    2012, 60(6), 2899-2912. DOI
  20. Shrinkage-to-tapering estimation of large covariance matrices.
    Xiaohui Chen
    , Z. Jane Wang, Martin J. McKeown.
    IEEE Transactions on Signal Processing, 
    2012, 60(11), 5640-5656DOI
  21. A two-graph guided multi-task Lasso approach for eQTL mapping.
    Xiaohui Chen
    , Xinghua Shi, Xing Xu, Zhiyong Wang, Ryan E. Mills, Charles Lee, Jinbo Xu.
    Journal of Machine Learning Research W&CP, 22, 208-217. AISTATS’12, La Palma, Canary Islands, April 2012. JMLR link. MATLAB Toolbox
  22. Large covariance matrices estimation: bridging shrinkage and tapering approaches.
    Xiaohui Chen
    , Z. Jane Wang, Martin J. McKeown.
    Kyoto, Japan, March 2012. DOI
  23. A Bayesian Lasso via reversible-jump MCMC.
    Xiaohui Chen, Z. Jane Wang, Martin J. McKeown.
    Signal Processing
    , 2011, 91(8), 1920-1932. DOI
  24. Asymptotic analysis of robust LASSOs in the presence of noise with large variance.
    Xiaohui Chen, Z. Jane Wang, Martin J. McKeown.
    IEEE Transactions on Information Theory, 2010, 56(10), 5131-5149. DOI
  25. fMRI group studies of brain connectivity via a group robust LASSO.
    Xiaohui Chen
    , Z. Jane Wang, Martin J. McKeown.
    ICIP’10, Hongkong, September 2010. DOI
  26. Asymptotic analysis of the Huberized LASSO estimator.
    Xiaohui Chen, Z. Jane Wang, Martin J. McKeown.
    ICASSP’10, Dallas, TX, USA, March 2010. DOI
  27. A sparse unified structural equation modeling approach for brain connectivity analysis.
    Xiaohui Chen, Z. Jane Wang, Martin J. McKeown.
    iCBBE’09, Beijing, China, June 2009. pdf
  28. An MM-based optimization algorithm for sparse linear modeling on microarray data analysis.
    Xiaohui Chen, Raphael Gottardo.
    iCBBE’09, Beijing, China, June 2009. DOI
  29. BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network.
    Xiaohui Chen, Ming Chen, Kaida Ning.
    Bioinformatics, 2006, 22(23), 2952-2954. DOI