Yi Li, PhD

Yi Li is a Professor of Biostatistics. His current research interests are survival analysis, longitudinal and correlated data analysis, measurement error problems, spatial models and clinical trial designs. His group is developing methodologies for analyzing large-scale and high-dimensional datasets, with direct applications in observational studies as well in genetics/genomics. His methodologic research is funded by various NIH statistical grants starting from year 2003. Yi Li is actively involved in collaborative research in clinical trials and observational studies with researchers from the University of Michigan and Harvard University. The applications have included chronic kidney disease surveillance, organ transplantation, cancer preventive studies and cancer genomics.

• Postdoctor, Biostatistics, Harvard, 1999-2000

• PhD, Biostatistics, University of Michigan, 1999

• M.S., Biostatistics, University of Michigan, 1996

• Sun, Y., Kang, J., Brummett, C. and Li, Y. (2022) Individualized risk assessment of pre-operative opioid use by interpretable neural network regression. Annals of Applied Statistics, in press.

• Salerno, S. and Li, Y.  (2022) High-dimensional survival analysis: methods and applications. Annual Review of Statistics and Its Application, in press.

• Zhang, E. and  Li, Y. (2022) High dimensional Gaussian graphical regression models with covariates. Journal of the American Statistical Association, in press.

• Salerno, S., Messana, J., Gremel, G., Dahlerus, C., Hirth, R., Han, P., Segal, J., Xu, T., Shaffer, D., Jiao, A., Simon, J., Tong, L., Wisniewski, K., Nahra, T., Padilla, R., Sleeman, K., Shearon, T., Callard, S., Yaldo, A., Borowicz, L., Agbenyikey, W., Horton, G., Roach, J. and Li, Y. (2021) COVID-19 risk factors and mortality outcomes among Medicare patients receiving long-term dialysis. JAMA Network Open, in press.

• Salerno, S., Sun, Y., Morris, E., He, X., Li, Y., Pan, Z., Han, P., Kang, J., Sjoding, M. and Li, Y. (2021) Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: racial disparities in healthcare utilization and outcomes. PLOS ONE, 16(10): e0258278. https://doi.org/10.1371/journal.pone.0258278.

• Xia, L., Nan, B. and Li, Y. (2021) De-biased lasso for generalized linear models with a diverging number of covariates. Biometrics, in press. doi:10.1111/biom.13587

• Fei, Z., Zheng, Q., Hong, H. and Li, Y. (2021) Inference for high dimensional censored quantile regression. Journal of the American Statistical Association, in press. doi:10.1080/01621459.2021.1957900      

• Fei, Z. and Li, Y. (2021) Estimation and inference for high dimensional generalized linear models: a splitting and smoothing approach. Journal of Machine Learning Research, 22, 1-32.   

• Hong, H. and Li, Y. (2020) Estimation of time-varying reproduction numbers underlying epidemiological processes: a new statistical tool for the COVID-19 pandemic. PLOS ONE, 15(7):e0236464.

• Zheng, Q., Hong, H. and Li, Y. (2020) Building generalized linear models with ultrahigh dimensional features: a sequentially conditional approach. Biometrics, 76, 47-60

• Zhe Fei, Ji Zhu, Moulinath Banerjee, Yi Li (2019). Drawing inferences for High‐dimensional Linear Models: A Selection‐assisted Partial Regression and Smoothing Approach Biometrics,  75, 551-61