Publications

* indicates student authors

*Kramer, A.,  Berral-González, A., Ellwood, K., Ding, S., Rivas, J., and Dutta, A. (2024). Cross-species Regulatory Network Analysis Identifies FOXO1 as a Driver of Ovarian Follicular Recruitment. Scientific Reports. Accepted. 

*Lemanski, E.,  Collins, B., Ebenezer, A., Anilkumar, S., Langdon, V., Zheng, Q., Ding, S., Franke, K., R., Schwarz, J., and Wright-Jin, E. (2024). A Novel Non-Invasive Murine Model of Neonatal Hypoxic-Ischemic Encephalopathy Demonstrates Developmental Delay and Motor Deficits with Activation of Inflammatory Pathways in Monocytes. Cells.  Accepted.

*Li, R., Ding, S., Ndura, K., and Jurkovitz, C. (2024). Building a Multistate Model from Electronic Health Records data for Predicting Long-term Diabetes Complications. Journal of Clinical and Translational Research. Accepted. 

Zhao, Y., Van Keilegom, I., and Ding, S. (2022). Envelopes for censored quantile regression. Scandinavian Journal of Statistics. DOI: https://doi.org/10.1111/sjos.12602.

Gavali, S.*, Chen, C., Cowart, J., Peng, X., Ding, S., Wu, C., and Anderson, T. (2021). Evaluation of machine learning methods to predict neighborhoods at a high risk of opioid abuse. IEEE Bioinformatics and Biomedicine, 1309-1314.

Xin, L. and Ding, S. (2021). Expected Returns with Leverage Constraints and Target Returns. Journal of Asset Management, 22, 200-208.

Zia, A., Ding, S., Messer, K. D., Miao, H., Suter, J., Fooks, J. R., Guilfoos, T., Tranda.r, S., Uchida, E., Tsai, Y., Merrill, S., Turnbull, S., and Koliba, C. (2020). Characterizing Heterogeneous Behavior of Non-Point Source Polluters in a Spatial Game under Alternate Sensing and Incentive Designs.  Journal of Water Resources Planning and Management, 146(8), 04020054.

Ding, S., Qian, W., and Wang, L. (2020). Double-slicing assisted sufficient dimension reduction for high dimensional censored data. Annals of Statistics, 48(4), 2132-2154. [PDF]

Chen, T.*, Su, Z., Yang, Y., and Ding, S. (2020). Efficient Estimation in Expectile Regression Using Envelope Models.

Electronic Journal of Statistics, 14, 143-173. [PDF]

Ding, S., Su, Z., Zhu, G, and Wang, L. (2019). Envelope Quantile Regression. Statistica Sinica. In press.  [PDF]  [Supplement material]

Qian, W., Ding, S., and Cook, R. D. (2019). Sparse minimum discrepancy approach to sufficient dimension reduction with simultaneous variable selection in ultrahigh dimension. Journal of American Statistical Association, 114, 1277-1290. [PDF]

Jain, Y.*, Ding, S. and Qiu, J. (2018). Slice inverse regression for integrative multi-omics data analysis. Statistical Applications in Genetics and Molecular Biology. In press.  [PDF]

Wang, L.* and Ding, S. (2018). Vector-autoregression and envelope model.  Stat, 7, e203:1-17.  [PDF]

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.  [PDF]

Supplement to "Matrix variate regressions and envelope models", Journal of the Royal Statistical Society: Series B, 1-36.  [PDF]

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.

Cai, Y.*, Fu, Z.*, Zhao, Y.,* Hu, Y.*, and Ding, S. (2017). Comparison of Statistical Learning and Predictive Models on Breast Cancer Data and King County Housing Data. APEC Research Reports; RR17-10, 1-34. URI: http://udspace.udel.edu/handle/19716/21667.

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. [PDF]

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

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

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.  [PDF]

My research has been supported by NSF DMS, DE-CTR ACCEL, NIH NIGMS, and State of Delaware.