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Runze Li

Eberly Family Chair Professor in Statistics
Runze Li


Runze Li is Eberly Family Chair Professor of Statistics at Penn State.

Li received his Ph.D. in Statistics from University of North Carolina at Chapel Hill in 2000.

Li's research interest includes variable selection and feature screening for high dimensional data, nonparametric modeling and semiparametric modeling and their application to social behavior science research. He is also interested in longitudinal data analysis and survival data analysis and their application to biomedical data analysis.

Li joined Penn State as an assistant professor of statistics in 2000, and became associate professor, full professor, distinguished professor and Verne M. Willaman Professor of Statistics in 2005, 2008, 2012 and 2014, respectively. Since 2018, he is the Eberly Family Chair Professor of Statistics. He received his NSF Career Award in 2004. He is a fellow of IMS, ASA and AAAS. He was co-editor of Annals of Statistics, and served as associate editor of Annals of Statistics and Statistica Sinica. He currently serves as associate editor of JASA and Journal of Multivariate Analysis.


Honors and Awards

  • The United Nations' World Meteorological Organization Gerbier-Mumm International Award for 2012
  • Editor of The Annals of Statistics (2013 - 2015)
  • Highly Cited Researcher in Mathematics (2014 - )
  • ICSA Distinguished Achievement Award, 2017
  • Faculty Research Recognition Awards for Outstanding Collaborative Research. College of
    Medicine, Penn State University, 2018
  • IMS Medallion Lecturer at Joint Statistical Meetings, August 5-10, 2023 in Toronto
  • Distinguished Mentoring Award, Eberly College of Science, Penn State University, 2023
  • Fellow, IMS, ASA and American Association for the Advancement of Science



  • Zhong, W., Qian, C., Liu, W., Zhu, L. and Li, R. (2023). Feature screening for interval-valued response with application to study association between posted salary and required skills. Journal of American Statistical Association. 118, 805 - 817.
  • Sheng, B., Li, C., Bao, L. and Li, R. (2023). Probabilistic HIV recency classification - a logistic regression without labelled individual level training data. Annals of Applied Statistics. 17, 108-129.
  • Guo, X, Li, R, Liu, J. and Zeng, M. (2023). Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic. Journal of Econometrics. 235, 166-179.
  • Bao, L, Li, C., Li, R. and Yang, S. (2022). Causal structural learning on MPHIA individual dataset. Journal of American Statistical Association. 117, 1642-1655.
  • Li, C. and Li, R. (2022). Linear hypothesis testing in linear models with high dimensional responses. Journal of American Statistical Association. 117, 1738-1750.
  • Guo, X., Li, R., Liu, J. and Zeng, M. (2022). High-dimensional mediation analysis for selecting DNA methylation Loci mediating childhood trauma and cortisol stress reactivity. Journal of American Statistical Association. 117, 1110-1121.
  • Nandy, D., Chiaromonte, F. and Li, R. (2022). Covariate information number for feature screening in ultrahigh-dimensional supervised problems. Journal of American Statistical Association. 117, 1516 - 1529.
  • Ren, H., Zou, C., Chen, N. and Li, R. (2022). Large-scale data streams surveillance via pattern-oriented-sampling. Journal of American Statistical Association. 117, 794-808.
  • Liu, W., Ke, Y., Liu, J. and Li, R. (2022). Model-free feature screening and FDR control with knockoff features. Journal of American Statistical Association. 117(537), 428-443.
  • Zou, T, Lan, W, Li, R. and Tsai, C.-L. (2022). Inference on Covariance-Mean Regression. Journal of Econometrics. 230, 318 - 338.
  • Liu, W., Yu, X. and Li, R. (2022). Multiple-splitting project test for high dimensional mean vectors. Journal of Machine Learning and Research. 23(71), 1-27.
  • Cai, Z., Li, R. and Zhang, Y. (2022). A distribution free conditional independence test with applications to causal discovery. Journal of Machine Learning and Research. 23(85), 1-41.
  • Shi, C., Song, R., Lu, W. and Li, R. (2021). Statistical inference for high-dimensional models via recursive online-score estimation. Journal of American Statistical Association. 116, 1307 - 1318.
  • Li, Z., Wang, Q. and Li, R. (2021). Central limit theorem for linear spectral statistics of large dimensional Kendall's rank correlation matrices and its applications. Annals of Statistics. 49, 1569 -1593.
  • Xiao, D., Ke, Y. and Li, R. (2021). Homogeneity structure learning in large-scale panel data with heavy-tailed errors. Journal of Machine Learning Research. 22 22(13):1-42, 2021.
  • Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020). A tuning-free robust and efficient approach to high-dimensional regression (with discussions and rejoinder). Journal of American Statistical Association. 115, 1700 - 1729.
  • Fang, X. E., Ning, Y. and Li, R. (2020). Test of signi cance for high-dimensional longitudinal data. Annals of Statistics. 48, 2622 - 2645.
  • Zhou, T., Zhu, L., Xu, C. and Li, R. (2020). Model-free forward regression via cumulative divergence. Journal of American Statistical Association. 115, 1393 - 1405.
  • Zou, C., Wang, G. and Li, R. (2020). Consistent selection of the number of change-points via sample-splitting. Annals of Statistics. 48, 413-439.
  • Cui, X., Li, R., Yang, G. and Zhou, W. (2020). Empirical likelihood test for large dimensional mean vector. Biometrika. 107, 591 - 607.
  • Wang, L., Chen, Z., Wang, C. D. and Li, R. (2020). Ultrahigh dimensional precision matrix estimation via refitted cross validation. Journal of Econometrics. 215, 118-130.
  • Chu, W., Li, R., Liu, J. and Reimherr, M. (2020). Feature screening for generalized varying coefficient mixed effect models with application to obesity GWAS. Annals of Applied Statistics. 14, 276 - 298.
  • Cai, Z, Li, R. and Zhu, L. (2020). Online sufficient dimension reduction through sliced inverse regression. Journal of Machine Learning and Research. 21(10), 1-25.
  • Li, X., Li, R., Xia, Z. and Xu, C. (2020). Distributed feature screening via componentwise debiasing. Journal of Machine Learning and Research. 21 (24), 1-32
  • Zhong, P.-S., Li, R. and Santo, S. (2019). Homogeneity test of covariance matrices and change-points identification with high-Dimensional longitudinal data. Biometrika. 106, 619 - 634.
  • Zheng, S., Chen, Z., Cui, H. and Li, R. (2019). Hypothesis testing on linear structures of high dimensional covariance matrix. Annals of Statistics. 47, 3300 - 3334.
  • Shi, C., Song, R., Chen, Z. and Li, R. (2019). Linear hypothesis testing for high dimensional generalized linear models. Annals of Statistics. 47, 2671 - 2703.
  • Liu, H., Wang, X., Yao, T., Li, R. and Ye, Y. (2019). Sample average approximation with sparsity-inducing penalty for high-dimensional stochastic programming. Mathematical Programming, 78, 69-108.
  • Chu, W., Li, R. and Reimherr, M. (2016). Feature screening for time-varying coefficient models with ultrahigh dimensional longitudinal data. Annals of Applied Statistics, 10, 596 - 617.
  • Li, R., Zhong, W. and Zhu, L. (2012). Feature screening via distance correlation learning. Journal of American Statistical Association. 107, 1129 - 1139.
  • Zou, H. and Li, R. (2008). One-step sparse estimates in nonconcave penalized likelihood models (with discussion). Annals of Statistics, 36, 1509-1566.
  • Li, R. and Liang, H. (2008). Variable selection in semiparametric regression modeling. Annals of Statistics. 36, 261-286
  • Fan, J. and Li, R. (2006). Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery. Proceedings of the International Congress of Mathematicians (M. Sanz-Sole, J. Soria, J.L. Varona, J. Verdera, eds.), Vol. III, European Mathematical Society, Zurich, 595-622.
  • Li, R. and Sudjianto, A. (2005). Analysis of computer experiments using penalized likelihood in Gaussian kriging Models. Technometrics. 47, 111-120.
  • Fan, J. and Li, R. (2004). New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. Journal of American Statistical Association, 99, 710-723.
  • Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and it oracle properties, Journal of American Statistical Association. 96, 1348-1360.
  • Cai, Z., Fan, J. and Li, R. (2000). Efficient estimation and inferences for varying coefficient models. Journal of the American Statistical Association. 5, 888-902.



Stat 565 - Multivariate Analysis

Stat 597 - Statistical Foundations of Data Science

Stat 597 - Statistical Inference on High-dimensional Data