Quefeng Li (李悫风)

Department of Biostatistics
3105D McGavran-Greenberg Hall
Chapel Hill, NC, 27599
Phone: (919)962-6450
Email: quefeng AT email DOT unc DOT edu

Professional Experience

  • Assistant Professor, Department of Biostatistics, UNC-Chapel Hill, 2015-present.
  • Faculty Fellow, SAMSI, 2016-2017.
  • Postdoctoral Research Associate, ORFE, Princeton University, 2013-2015.


Research Interest

  • High dimensional data analysis with applications in biomedical research
  • Data integration
  • Robust statistics


Statistical Methodology

  • Avella-Medina, M., Battey, H., Fan, J., and Li, Q. (2018+). Robust Estimation of High Dimensional Covariance and Precision Matrices. Biometrika, to appear. [pdf] [supp]
  • Li, Q., Cheng, G., Fan, J., and Wang, Y. (2018+). Embracing the Blessing of Dimensionality in Factor Models. Journal of the American Statistical Association, to appear. [pdf] [supp]
  • Li, Q., Yu, M., and Wang, S. (2017). A Statistical Framework for Pathway and Gene Identification from Integrative Analysis. Journal of Multivariate Analysis, 156:1-17. [pdf]
  • Fan, J., Li, Q., and Wang, Y. (2017). Estimation of High-Dimensional Mean Regression in Absence of Symmetry and Light-tail Assumptions. Journal of the Royal Statistical Society: Series B, 1:247-265. [pdf] [supp]
  • Li, Q. and Shao, J. (2015). Regularizing LASSO: A Consistent Variable Selection Method. Statistica Sinica, 25:975-992. [pdf] [supp]
  • Li, Q. and Shao, J. (2015). Sparse Quadratic Discriminant Analysis for High Dimensional Data. Statistica Sinica, 25:457-473. [pdf] [supp]
  • Xu, Y., Yu, M., Zhao, Y. Q., Li, Q., Wang, S., and Shao, J. (2015). Regularized Outcome Weighted Subgroup Identification for Differential Treatment Effects. Biometrics, 71:645-653. [pdf] [supp] [R code]
    {An earlier version received 2014 John Van Ryzin Award.}
  • Li, Q., Wang, S., Huang, C., Yu, M., and Shao, J. (2014). Meta-Analysis Based Variable Selection for Gene Expression Data. Biometrics, 70:872-880. [pdf] [supp] [R code]
  • Yu, M. and Li, Q. (2014). Discussion of "Combining Biomarkers to Optimize Patient Treatment Recommendations". Biometrics, 70:716-719. [pdf]

Collaborative Work

  • Wu, J., Cummings, D., Li, Q., Halladay, J., Donahue, K., Cene, C., M.D., Hinderliter, A., Bosworth, H., Miller, C., Garcia, B., Tillman, J., DeWalt, D. (2018+). The Effect of a Practice-based Multi-component Intervention that Includes Health Coaching on Medication Adherence and Blood Pressure Control in Rural Primary Care. Journal of Clinical Hypertension, to appear.
  • Jones, S., Li, Q., Aiello, A., O'Rand, A., Turkbey E., Roux, A., and Evenson, K. (2018+). Physical Activity, Sedentary Behavior, and Retirement: the Multi-Ethnic Study of Atherosclerosis (MESA). The American Journal of Preventive Medicine, to appear.
  • Halladay, J., Donahue K., Cene C.W., Li, Q., Cummings, D., Hinderliter, A., Miller, C., Garcia B., Little, E. Tillman, J., Ammerman, A., DeWalt, D. (2016). The Association of Health Literacy and Blood Pressure Reduction in a Cohort of Patients with Hypertension: The Heart Healthy Lenoir Trial. Patient Education and Counseling. [PubMed]
  • Scerpella, T.A., Bernardoni, B., Wang, S., Rathouz P.J., Li, Q., and Dowthwaite, J.N. (2016). Site-Specific, Adult Bone Benefits Attributed to Loading During Youth: A Preliminary Longitudinal Analysis. Bone, 85:148-159. [PubMed]
  • Bernardoni, B., Scerpella, T.A., Rosenbaum, P.F., Kanaley, J.A., Li, Q., Wang, S. and Dowthwaite, J.N. (2015). The Influence of Organized Physical Activity (Including Gymnastics) on Young Adult Skeletal Traits: Is Maturity Phase Important? Pediatric Exercise Science, 27:285-296. [PubMed]
  • Bernardoni, B., Thein-Nissenbaum, J., Fast, J., Day, M., Wang, S., Li, Q., and Scerpella, T.A. (2014). A School-Based Resistance Intervention Improves Skeletal Growth in Adolescent Females. Osteoporosis International, 25:1025-1032. [PubMed]
  • Coursin, D., Head, D., Chen, G., Li, Q., Wang, S. and Hogan, K. (2014). Vitamin D Deficiency in Anesthesia Caregivers at the End of Winter. Acta Anaesthesiologica Scandinavica, 58:802-806. [PubMed]


  • MetaLasso: An integrative generalized linear model (GLM) running over multiple high-dimensional datasets to select variables and groups of variables. One application is pathway/gene selections from an integrative GLM over multiple genomic studies. [manual]

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