Shanshan Ding

Department of Applied Economics and Statistics

Shanshan Ding

Associate Professor of Statistics

Office Location:
531 S. College Avenue
225 Townsend Hall
Newark, DE 19716



  • Ph.D., Statistics, University of Minnesota, Minneapolis, 2014
  • M.S., Applied and Computational Mathematics, University of Minnesota, Duluth, 2008
  • M.S., Finance, Peking University, 2004
  • B.S., Applied Mathematics, Nankai University, 2002

Currently Teaching


  • STAT 617-010 – Multivariate Methods

Research Interests


Dimension reduction, high dimensional data analysis,  multivariate analysis, envelope models, imaging data analysis, longitudinal data analysis, econometrics, health and environmental applications.


Qian, W., Ding, S., and Cook, R. D. (2018). Sparse minimum discrepancy approach to sufficient dimension reduction with simultaneous variable selection in ultrahigh dimension. Journal of American Statistical Association. 1-48.

Wang, L.* and Ding, S. (2018). Vector-autoregression and envelope model.  Stat. Accepted.

Ding, S. and Cook, R. D. (2018). Matrix variate regressions and envelope models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80, 387-408.

Supplement to “Matrix variate regressions and envelope models”, Journal of the Royal Statistical Society: Series B, 1-36.

Zia, A., Messer, K. D., Ding, S., Miao, H., Suter, J., Fooks, J. R., Guilfoos, T., Trandafir, S., Uchida, E., Tsai, Y., Merrill, S., Turnbull, S., and Koliba, C. (2016). Spatial effects of sensor information in inducing cooperative behaviors for managing non-point source pollution: Evidence from a decision game in an idealized watershed. Preprint.

Jain, Y.* and Ding, S. (2017). An integrative sufficient dimension reduction method for multi-omics data analysis. Proceedings of ACM BCB. Accepted.

Ding, S. and Cook, R. D. (2015). Tensor sliced inverse regression. Journal of Multivariate Analysis. 133, 216-231.

Ding, S. and Cook, R. D. (2015). Higher-order sliced inverse regression. Wiley Interdisciplinary Reviews: Computational Statistics. 7, 249-257.

Ding, S. and Cook, R. D. (2014). Dimension folding PCA and PFC for matrix-valued predictors. Statistica Sinica, 24, 463-492.

Ding, S. and Sinha, M. (2011). Evaluation of power of different Cox proportional hazards models incorporating stratification factors. In JSM Proceedings. Miami, FL: American Statistical Association, 4307-4320.

*Student authors