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. https://www.nature.com/articles/s41598-024-80003-2.

*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.  https://pubmed.ncbi.nlm.nih.gov/39329733/.

*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. https://pubmed.ncbi.nlm.nih.gov/39345707/. 

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). Understanding the factors related to the opioid epidemic using machine learning.  IEEE Bioinformatics and Biomedicine, 1309-1314. https://par.nsf.gov/servlets/purl/10332349.

Ding, S., Su, Z., Zhu, G, and Wang, L. (2021). Envelope Quantile Regression. Statistica Sinica. https://www3.stat.sinica.edu.tw/statistica/j31n1/j31n104/j31n104.html.  [Supplement material]

Xin, L. and Ding, S. (2021). Expected Returns with Leverage Constraints and Target Returns. Journal of Asset Management, 22, 200-208.  https://par.nsf.gov/servlets/purl/10332350.

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.  https://projecteuclid.org/journals/annals-of-statistics/volume-48/issue-4/Double-slicing-assisted-sufficient-dimension-reduction-for-high-dimensional-censored/10.1214/19-AOS1880.full.

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. DOI: https://doi.org/10.1214/19-EJS1664.

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.  https://ascelibrary.org/doi/10.1061/%28ASCE%29WR.1943-5452.0001242.

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. https://www.tandfonline.com/doi/abs/10.1080/01621459.2018.1497498.

Jain, Y.*, Ding, S. and Qiu, J. (2019). Slice inverse regression for integrative multi-omics data analysis. Statistical Applications in Genetics and Molecular Biology. https://pubmed.ncbi.nlm.nih.gov/30685747/.

Wang, L.* and Ding, S. (2018). Vector-autoregression and envelope model.  Stat, 7, e203:1-17.  https://onlinelibrary.wiley.com/doi/10.1002/sta4.203.

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.  https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12247.

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

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. 

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.

Ding, S. and Cook, R. D. (2015). Tensor sliced inverse regression. Journal of Multivariate Analysis. 133, 216-231. https://www.sciencedirect.com/science/article/pii/S0047259X14001985.

Ding, S. and Cook, R. D. (2015). Higher-order sliced inverse regression. Wiley Interdisciplinary Reviews: Computational Statistics. 7, 249-257.  https://dl.acm.org/doi/abs/10.1002/wics.1354. 

Ding, S. and Cook, R. D. (2014). Dimension folding PCA and PFC for matrix-valued predictors. Statistica Sinica, 24, 463-492.  https://www3.stat.sinica.edu.tw/sstest/oldpdf/A24n124.pdf.

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.