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.

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.

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.

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.

Ding, S., Su, Z., Zhu, G, and Wang, L. (2020). Envelope Quantile Regression. Statistica Sinica. https://www3.stat.sinica.edu.tw/statistica/j31n1/j31n104/j31n104.html.  [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. 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]

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. 

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.