Professor of Statistics

Institute of Natural Sciences

Shanghai Jiao Tong University

800 Dongchuan Road

Minhang, Shanghai

Phone: (86-21) 54742760

Office: Room 514,Pao Yue-Kong Library(North Gate Entrance)

E-mail: weidongl@sjtu.edu.cn

- 2011-- Professor, Institute of Natural Sciences and Department of Mathematics, Shanghai Jiao Tong University
- 2009--2011 Postdoctoral Fellow, Department of Statistics, The Wharton SchoolUniversity of Pennsylvania
- 2008--2009 Postdoctoral Fellow, Department of Mathematics, Hong Kong University of Science and Technology
- 2003--2008 Ph.D. in Mathematics, Zhejiang University, P.R. China

- National Excellent Doctoral Dissertation Award from China, 2010
- New World Mathematics Awards, Silver Award for the Ph.D Thesis, 2010.

- High dimensional statistical inference
- Statistical methods for biostatistics
- Nonlinear time series analysis
- Nonparametric modeling
- New statistical methods

- 1. Chen, X., Liu, W.D. and Zhang, Y. (2018). Quantile Regression under Memory Constraint. Technical report.
- 1. Liu, W.D., Leung, D. and Shao, Q.M. (2018). False discovery control for pairwise comparisons - an asymptotic solution to Williams, Jones and Tukey's conjecture. Technical report. [pdf]
- 2. Chen X. and Liu, W.D. (2017). Statistical inference for matrix-variate Gaussian graphical models and false discovery rate control. Statistica Sinica, to appear.
- 3. Liu, W.D. (2017). Structural similarity and difference testing on multiple sparse Gaussian graphical models. Annals of Statistics, to appear. [matlab code]
- 4. Cai, T. and Liu, W.D. (2016), Large-Scale Multiple Testing of Correlations. Journal of the American Statistical Association, 111, 229-240.[pdf]
- 5. Liu, W.D. (2014), Incorporation of Sparsity Information in Large-scale Multiple Two-sample t Tests. Technical report. [pdf] [matlab code] [matlab code]
- 6. Liu, W.D. and Luo, S. (2014), Hypothesis Testing for High-dimensional Regression Models. Technical report. [pdf]
- 7. Liu, W.D. and Shao, Q.M. (2014), Phase Transition and Regularized Bootstrap in Large-scale t-tests with False Discovery Rate Control. Annals of Statistics, 42, 2003-2025. [matlab code]
- 8. Liu, W.D. (2013), Gaussian Graphical Model Estimation with False Discovery Rate Control. Annals of Statistics, 41, 2948-2978. [matlab code]
- 9. Liao, K. MD, Sparks, J., Hejblum, B., Kuo, I., Cui, J., Lahey, L., Cagan, A., Gainer, V., Liu, W.D., Cai, T.T., Sokolove, J.,Cai, T. (2017). Phenome-wide association study of autoantibodies to citrullinated and non-citrullinated epitopes in rheumatoid arthritis. Arthritis & Rheumatology, 69, 742-749.
- 1. Cai, T., Liu, W.D. and Zhou, H.B. (2016), Estimating Sparse Precision Matrix: Optimal Rates of Convergence and Adaptive Estimation. Annals of Statistics, 44, 455-488.
- 2. Cai, T., Cappola, T., Li, H., Liu, W.D. and Xie, J. (2016), Joint Estimation of Multiple High-dimensional Precision Matrices. Statistica Sinica, 26, 445-464.
- 3. Liu, W.D. and Luo, X. (2015), Fast and adaptive sparse precision matrix estimation in high dimensions. Journal of Multivariate Analysis, 135, 153-162
- 4. Tony Cai, Hongzhe Li, Weidong Liu, and Jichun Xie (2013),Covariate Adjusted Precision Matrix Estimation with an Application in Genetical Genomics. Biometrika, 100: 139-156. [pdf]
- 5. Cai, T. and Liu, W.D. (2011), A Direct Estimation Approach to Sparse Linear Discriminant Analysis. Journal of the American Statistical Association. 106: 1566-1577. [pdf] [matlab code]
- 6. Cai, T.,Liu, W.D. and Luo, X. (2011), A constrained L1 minimization approachto sparse precision matrix estimation. Journal of the American Statistical Association. 106: 594-607. [pdf]
- 7. Cai, T. and Liu, W.D. (2011), Adaptive thresholding for sparse covariance matrix estimation. Journal of the American Statistical Association. 106: 672-684.(with correction in the proof) [pdf][correction] [matlab code]
- 1. Cao,H., Liu, W.D. and Zhou, Z. (2017). Simultaneous nonparametric regression analysis of sparse longitudinal data. Bernoulli, to appear.
- 2. Chen, X. and Liu, W.D. (2017). Testing independence with high-dimensional correlated samples. Annals of Statistics, to appear.
- 3. Degui Li, Weidong Liu, Qiying Wang and Weibiao Wu (2016),Simultaneous Inference for Nonlinear Cointegration Models. Statistica Sinica, to appear.
- 4. Tony Cai, Weidong Liu and Yin Xia (2014),Two-Sample Test of High Dimensional Means under Dependency. Journal of the Royal Statistical Society - Series B, 76, 349-372.
- 5. Tony Cai, Weidong Liu and Yin Xia (2013),Two-Sample Covariance Matrix Testing And Support Recovery in High-dimensional and Sparse Settings. Journal of the American Statistical Association, 108: 265-277. [pdf] [Supplement]
- 6. Liu, W.D. and Wu, W.B. (2010), Simultaneous nonparametric inference of time series. The Annals of Statistics. 38: 2388-2421. [pdf]
- 7. Liu, W.D. and Wu, W.B. (2010). Asymptotics of spectral density estimates. Econometric Theory. 26: 1218-1245. [pdf]
- 8. Li, D.L., Liu, W.D. and Rosalsky, A. (2010). Necessary and sufficient conditions for the asymptotic distribution of the largest entry of a sample correlation matrix. Probability Theory and Related Fields. 148: 5-35. [pdf]
- 9. Lin, Z.Y. and Liu, W.D. (2009), On maxima of periodograms of stationary processes, The Annals of Statistics, 37:2676-2695. [pdf]
- 10. Liu, W.D., Lin, Z.Y. and Shao, Q.M. (2008), The asymptotic distribution and Berry-Esseen bound of a new test for independence in high dimension with an application to stochastic optimization, The Annals of Applied Probability, 18: 2337-2366. [pdf]
- 1. Istvan Berkes, Weidong Liu and Wei Biao Wu (2014),Komlos-Major-Tusnady approximation under dependence. Annals of Probability, 42, 794-817. [pdf]
- 2. Liu, W.D., Chan, N. and Wang, Q. (2014). Uniform approximation to local time with applications in non-linear cointegrating regression. Published in a book by Wang, Q. [pdf]
- 3. Liu, W.D., Ling, S. and Shao, Q.M. (2011), On non-stationary threshold autoregressive models. Bernoulli. 17: 969-986.
- 4. Liu, W.D. and Lin, Z.Y. (2009), Strong approximation for a class of stationary processes, Stochastic Processes and their Applications,119: 249-280. [pdf]
- 1. Weidong Liu and Qiman Shao (2013),A Cramer moderate deviation theorem for Hotelling T2-statistic with applications to global tests. Annals of Statistics, 41: 296-322.
- 2. Liu, W.D., Shao, Q.M. and Wang, Q. (2013), Self-normalized Cram\'{e}r type large deviations for the maximum of sums of independent random variables. Bernoulli, 19, 1006-1027. [pdf]
- 3. Liu, W.D. and Shao, Q.M. (2010), Cramer type moderate deviation for the maximum of the periodogram with application to simultaneous tests. The Annals of Statistics. 38: 1913-1935. [pdf]

The most difficult aspect for the maximum type statistics is deriving their distributions under dependence. The main techniques in these works can be traced back to Liu, Lin and Shao (2008,AAP),Lin and Liu (2009, AOS), Liu and Wu (2010, ET,AOS).

- lecture1 [pdf]
- lecture2 [pdf]
- t statistics [pdf]
- bootstrap [pdf]
- lecture-3 and code [rar]
- lecture-4 and code [rar]
- lecture-5 and code [rar]
- lecture-6 and code [rar]
- lecture-7 [rar]
- lecture-8 [pdf]
- lecture-9 [rar]
- lecture-10 [pdf]
- lecture-11 [rar]
- If you have any question on this course, you can send me an email about the office hour.

- summer course [pdf]