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Bing Li

Verne M. Willaman Professor of Statistics
Bing Li


Bing Li is a Verne M. Willaman Professor of Statistics at Penn State.

Li received his Ph.D. in Statistics in 1992 from The University of Chicago, and received his M.Sc. in Statistics in 1989 from The University of British Columbia, Vancouver, Canada. Li completed his M.Sc. in System Sciences in 1986 at the Graduate School of Beijing Institute of Technology in Beijing, China, where he also received his B.Sc. in Automatic Control in 1982.




Honors and Awards




  • Zifang Guo, Lexin Li, Wenbin Lu, and Bing Li (2015). Groupwise dimension reduction via envelope method. To appear in Journal of the American Statistical Association. 
  • Bing Li, Hyonho Chun, and Hongyu Zhao (2014). On an additive semigraphoid model for statistical networks with applications to pathway analysis. Journal of the American Statistical Association109,1188-1204. 
  • Wei Luo, Bing Li, and Xiangrong Yin (2014). On efficient dimension reduction with respect to a statistical functional of interest. The Annals of Statistics42, 382-412.
  • Kuang-Yao Lee, Bing Li, Francesca Chiaromonte (2013). A general theory for nonlinear sufficient dimension reduction: formulation and estimation. The Annals of Statistics41. 221-249. 
  • Bing Li, Hyonho Chun, and Hongyu Zhao (2012). Sparse estimation of conditional graphical models with application to gene networks.  Journal of American Statistical Association107, 152-167.
  • Bing Li, Andreas Artemiou, and Lexin Li (2011). Principal support vector machines for linear and nonlinear sufficient dimension reduction. The Annals of Statistics39, 3182-3210.
  • Xiangrong Yin and Bing Li (2011). Sufficient dimension reduction based on an ensemble of minimum average variance estimators. The Annals of Statistics39, 3392-3416.
  • Bing Li, Min Kyung Kim, and Naomi Altman (2010). On dimension folding of matrix or array valued statistical objects. The Annals of Statistics38, 1094-1121.
  • Yuexiao Dong and Bing Li (2010). Dimension reduction for non-elliptically distributed predictors: second-order methods. Biometrika97, 279-294.
  • R. Dennis Cook, Bing Li, and Francesca Chiaromonte (2010). Envelope models for parsimonious and efficient multivariate linear regression (with discussion). Statistica Sinica20, 927-1010.