Principal Investigator, The Methodology Center
Verne M. Willaman Professor of Statistics
Professor, Public Health Sciences
The Methodology Center
The Pennsylvania State University
400 Calder Square II
State College, PA 16801
Ph.D., University of North Carolina at Chapel Hill, 2000 (Statistics)
M.S., Academica Sinica, Beijing, 1993 (Statistics)
B.S., Beijing Normal University, 1990 (Mathematics)
2014: Highly Cited Researcher in Mathematics based on data from Essential Science Indicators, 2002-2012
2013-2015: Co-Editor, Annals of Statistics
2012: Distinguished Professor, Statistics
2011: Fellow, American Statistical Association, 2011
Research Interests & Collaborations
Variable selection is fundamental to statistical modeling. In my work, new approaches are proposed to select significant variables for various statistical models. We have shown that the proposed approaches have oracle properties; namely, they work as well as if the correct submodel were known.
I also work on variable screening for datasets where the number of variables greatly exceeds the number of subjects, as is common in genetic data. Variable screening methods will enable behavioral researchers to focus their efforts on a reduced subset of predictors that have potential impact on an outcome.
My work on intensive longitudinal data focuses on the time-varying effect model (TVEM), which lets researchers see changes in relationships between variables without making assumptions about the nature of those relationships.
I am working on several related projects that apply nonparametric and semiparametric statistical modeling techniques to the analysis of intensive longitudinal data including ecological momentary assessment data.
My collaborators on these projects are Lisa Dieker (Wesleyan University), Xianming Tan (McGill University), Mariya Shiyko (Northeastern University), Anne Buu (University of Michigan), Linda Collins, Stephanie Lanza, John Dziak, and Sara Vasilenko.
Center for Prevention and Treatment Methodology
National Institute on Drug Abuse P50 (Renewal)
2010-2015; Role: Principal Investigator
Folded Concave Penalized Learning for Parkinson's Biomarkers Identification
Grace Woodward Collaborative Engineering/Medicine Research Grant
2014-2016; Role: Co-PI (PI: Tao Yao; Co-PI: Xumei Huang)
Pennsylvania State University Tobacco Center of Regulatory Science (TCORS)
National Institute on Drug Abuse
2013-2018; Role: Investigator (PI: Joshua Muscat)
Do Access Barriers to Autism Care Persist Despite Autism Insurance Mandate?
National Institute on Mental Health
2012-2015; Role: Investigator (PI: Li Wang)
Advancing Tobacco Research by Integrating Systems Science and Mixture Models
National Cancer Institute R01
2012-2015; Role: Investigator (PI: Stephanie Lanza)
Measurement and Methodology for Daily Patterns of Drug Use and Related Behaviors
National Institute on Drug Abuse
2013-2018; Role: PI of Subcontract (PI: Rebecca Cunningham)
Chen, Z., Li, R., & Li, Y. (in press). Varying coefficient models for data with auto-correlated error process. Statistica Sinica.
Li, J., Zhong, W., Li, R., & Wu, R. (in press). A fast algorithm for selecting gene-gene interactions in genome-wide association studies. Annals of Applied Statistics.
Pan, R., Wang, H., & Li, R. (in press). On the ultrahigh dimensional linear discriminant analysis problem with a diverging number of classes. Journal of the American Statistical Association.
Wang, L., Peng, B., & Li, R. (in press). A high-dimensional nonparametric multivariate test for mean vector. Journal of the American Statistical Association.
Wang, N., Wang, T., Han, H., Huber, K. J., Yang, J. - M., Li, R., & Wu, R. (in press). Modeling expression plasticity of genes that differentiate drug-sensitive from drug-resistant cells to chemotherapeutic treatment. Current Genomics.
Yang, H., Cranford, J., Li, R., & Buu, A. (in press). Two-stage model for time-varying effects of discrete longitudinal covariates with applications in the analysis of daily process data. Statistics in Medicine.
Zhang, X., Wu, Y., Wang, L., & Li, R. (in press). Variable selection for support vector machine in moderately high dimensions. Journal of the Royal Statistical Society.
Buu, A., Li, R., Walton, M., Yang, H., Zimmerman, M. A., & Cunningham, R. M. (2014). Changes in substance use-related health risk behaviors on timeline follow-back interview as a function of the length of recall period. Substance Use and Misuse, 49(10), 1259-1269.
Chen, H., Wang, Y., Li, R., & Shear, K. (2014). A note on nonparametric regression test through penalized splines. Statistica Sinica, 24, 1143-1160.
Cui, H., Li, R., & Zhong, W. (2014). Model-free feature screening for ultrahigh dimensional discriminant Analysis. Journal of the American Statistical Association. Advance online publication. doi: 10.1080/01621459.2014.920256
Dziak, J., Li, R., Zimmerman, M., & Buu, A. (2014). Time-varying effect models for ordinal responses with applications in substance abuse research. Statistics in Medicine, 33(29), 5126-37.
Huang, D., Li, R., & Wang, H. (2014). Feature screening for ultrahigh dimensional categorical data with applications. Journal of Business and Economic Statistics, 32(2), 237-244.
Kurum, E., Li, R., Wang, Y., & Senturk, D. (2014). Nonlinear varying coefficient model with applications to a photosynthesis study. Journal of Agricultural, Biological, and Environmental Statistics, 19(1), 57-81.
Lanza, S. T., Vasilenko, S., Liu, X., Li, R., & Piper, M. (2014). Advancing the understanding of craving during smoking cessation attempts: A demonstration of the time-varying effect model. Nicotine and Tobacco Research, 16, S127-134.
Liu, J., Li, R., & Wu, R. (2014). Feature selection for varying coefficient models with ultrahigh-dimensional covariates. Journal of the American Statistical Association, 109(505), 266-274.
Shiyko, M., Burkhalter, J. E., Li, R., & Park, B. J. (2014). Modeling nonlinear time-dependent treatment effects: An application of the generalized time-varying effect model (TVEM). Journal of Consulting and Clinical Psychology, 82(5), 760-772.
Shiyko, M., Naab, P., Shiffman, S., & Li, R. (2014). Modeling complexity of EMA data: Time-varying lagged effects of negative affect on smoking urges for subgroups of nicotine addiction. Nicotine & Tobacco Research, 16, S144-150.
Vasilenko, S. A., Piper, M. E., Lanza, S. T., Liu, X., Yang, J., & Li, R. (2014). Time-varying processes involved in smoking lapse in a randomized trial of smoking cessation therapies. Nicotine & Tobacco Research, 16, S135-143.
Wang, L., Sherwood, B., & Li, R. (2014). Discussion of "Estimation and Accuracy after Model Selection" by Brad Efron. Journal of the American Statistical Association, 109, 1007-1010.