Publications

Selected publications

(* indicates students advised)

  1. Jackknife multiplier bootstrap: finite sample approximations to the U-process supremum with applications.
    Xiaohui Chen and Kengo Kato.
    First version: August 2017.
  2. Finite sample change point inference and identification for high-dimensional mean vectors.
    Mengjia Yu* and Xiaohui Chen.
    First version: October 2017. (coming soon)
  3. Gaussian and bootstrap approximations for high-dimensional U-statistics and their applications.
    Xiaohui Chen.
    Annals of Statistics, to appear, 2017+. DOI
    (This paper supersedes the arXiv preprint “Gaussian approximation for the sup-norm of high-dimensional matrix-variate U-statistics and its applications.“)
  4. 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
  5. Inference of high-dimensional linear models with time-varying coefficients.
    Xiaohui Chen, Yifeng He*.
    Statistica Sinica, to appear, 2018, 28(1). DOI
  6. 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
  7. Discussion of “High-dimensional autocovariance matrices and optimal linear prediction.”
    Xiaohui Chen.
    Electronic Journal of Statistics, 2015, Vol. 9, 801-810. DOI
  8. A note on moment inequality for quadratic forms.
    Xiaohui Chen.
    Statistics and Probability Letters, 2014, Vol. 92, 83-88. DOI
  9. 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
  10. 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.
  11. 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
  12. 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
  13. 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
  14. Large covariance matrices estimation: bridging shrinkage and tapering approaches.
    Xiaohui Chen
    , Z. Jane Wang, Martin J. McKeown.
    ICASSP’12,
    Kyoto, Japan, March 2012. DOI
  15. A Bayesian Lasso via reversible-jump MCMC.
    Xiaohui Chen, Z. Jane Wang, Martin J. McKeown.
    Signal Processing
    , 2011, 91(8), 1920-1932. DOI
  16. 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
  17. 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
  18. Asymptotic analysis of the Huberized LASSO estimator.
    Xiaohui Chen, Z. Jane Wang, Martin J. McKeown.
    ICASSP’10, Dallas, TX, USA, March 2010. DOI
  19. 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
  20. An MM-based optimization algorithm for sparse linear modeling on microarray data analysis.
    Xiaohui Chen, Raphael Gottardo.
    iCBBE’09, Beijing, China, June 2009. DOI
  21. 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