Tao YU

Associate Professor
Department of Statistics & Applied Probability
National University of Singapore

Education

• • 2009 PhD,
    Statistics, University of Wisconsin, Madison

• • 2004 MS,
    Probability and Statistics, Nankai University

• 2001 BS,
  Mathematics, Nankai University

Research Interests

• Statistical Modeling of the Brain Imaging Data

   

• Theory and application of the Semi- and Non-parametric likelihood methods

   

• Shape Constrained Inference in Non-parametric Models

   

• Statistical Modeling of the High Throughput Gene Data

   

• Density Estimation in Multiple Sample Data

   

Selected Publications

• Chen, B., Li, P., Qin, J., and Yu, T. (Forthcoming). “Using a monotonic density ratio model to find the asymptotically optimal combination of multiple diagnostic tests.” Journal of the American Statistical Association, accepted.

   

• Yu, T. and Li, P. (2013). “Spatial shrinkage estimation of diffusion tensors on diffusion weighted imaging data”. Journal of the American Statistical Association, 108, 864-875.

   

• Yu, T., Zhang, C.M., Alexander, A.L., and Davidson, R.J. (2013). “Local Tests for Identifying Anisotropic Diffusion Areas in Human Brain with DTI”. The Annals of Applied Statistics, 7, 201-225.

   

• Yu, T., Li, J., and Ma, S. (2012). “Adjusting Confounders in Ranking Biomarkers: A model-based ROC approach”. Briefings in Bioinformatics, 13, 513-523.

   

• Zhang, C.M., Fan, J. and Yu, T. (2011). “Multiple testing via FDRL for large-scale imaging data”. The Annals of Statistics, 39, 613-642.

   

• Zhang, C.M. and Yu, T. (2008). “Semiparametric detection of significant activation for brain fMRI”. The Annals of Statistics , 36, 1693–1725.

   

• Zhang, C.M., Jiang, Y. and Yu, T. (2007). “A comparative study of one-level and two-level semiparametric estimation of hemodynamic response function for fMRI data”. Statistics in Medicine , 26, 3845–3861. (special issue on statistical analysis of neuronal data.)

   

• Zhang, C.M., Fu, H., Jiang, Y. and Yu, T. (2007). “High-dimensional pseudo logistic regression and classification with applications to gene expression data”. Computational Statistics and Data Analysis , 52, 452–470.(special issue on statistical learning methods including dimesionality reduction.)

   

Selected Works in Progress

• Yu, T., Li, P., and Qin, J., “Two-sample density estimation with likelihood ratio ordering” Rejected with Resubmission for Biometrika.

   

• Li, P., Yu, T., and Qin, J. “Likelihood Ratio Test for Homogeneity with Correlated Unordered Pairs”, Rejected with Resubmission for the Annals of Statistics.

   

Grants

• NUS Start-up Grant: “Local Tests for Analysing Human Brain Anatomical Structure on DT-MRI and DWI” S$60,000; PI; September 2009- September 2013.

   

• NUS AcRF Tier 1 Grant: “New Methods for High-throughput Gene Data”, S$49,500, PI; February 2013- July 2015.

   

• NUS AcRF Tier 1 Grant: “Regularization and Variable Selection in Computer Intensive Statistical Methods”, S$50,800, PI; August 2011 – July 2015.

   

• NUS AcRF Tier 1 Grant: “Sparse Portfolio Selection: Method, Theory, and Applications”, S$39,672, PI; March 2012 – February 2015.

   

• MOE Tier 2 grant: “Estimation and Modelling of Conditional Covariance Matrices”, S$268,965, CO-PI; March 2015 – February 2018.

   

• NUS AcRF Tier 1 Grant: “Pseudo-likelihood Method for Transformation and Box-Cox Transformation Model”, S$73,600, PI; June 2015 – May 2018.

   

Teaching

• ST2334 Probability and Statistics

   

• ST3131 Regression Analysis

   

• ST3232 Design and Analysis of Experiments

   

• ST4232 Nonparametric Statistics

   

• ST4242 Longitudinal Data Analysis

   

• ST5203 Experimental Design

   

• ST5206 Generalized Linear Models

   

• ST5222 Advanced Topics in Applies Statistics

   

Useful Links

• Department of Statistics and Applied Probability, NUS
• Department of Statistics, UW-Madison
• American Statistical Association (ASA).
• Institute of Mathematical Statistics (IMS).
• R Website.
• Matlab Documentation.