Research Assistant Professor, Survey Research Center, Institute for Social Research
Yajuan Si is a Research Assistant Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor campus. She received her Ph.D on Statistical Science in 2012 from Duke University. Before joining the University of Michigan in 2017, Yajuan was an assistant professor jointly in the Department of Biostatistics & Medical Informatics and the Department of Population Health Sciences at the University of Wisconsin-Madison and a Postdoctoral Research Scholar in the Department of Statistics at Columbia University. Dr Si’s research lies in cutting-edge methodology development in streams of Bayesian statistics, complex survey inference, missing data imputation, causal inference, and data confidentiality protection. Yajuan has extensive collaboration experiences with health services researchers and epidemiologists to improve healthcare and public health practice, and she has been providing statistical support to solve sampling and analysis issues on health and social science surveys.
- Collaborative Research: Multilevel Regression and Poststratification: A Unified Framework for Survey Weighted Inference
- Methodological Research on Mobile Technology in the Collection of Household Food Expenditure Data
- Profiling Missing Data in Electronic Health Records For Diabetes Care Research
- Statistical Methods for Healthcare in Complex Patients with Diabetes
- Makela, Susanna; Si, Yajuan; Gelman, Andrew (2018). Bayesian inference under cluster sampling with probability proportional to size. Statistics in Medicine, 37(26), 3849-3868.
- Si, Yajuan; Reiter, Jerome P. (2017). Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys. Journal of Educational and Behavioral Statistics, 38(5), 499-521.
- Makela, S.; Si, Yajuan; Gelman, A. (2017). Graphical Visualization of Polling Results. In Atkeson, Lonna R.; Alvarez, R. M. (Ed.), The Oxford Handbook of Polling and Polling Methods. Oxford University Press:Oxford, United Kingdom.
- Si, Yajuan; Reiter, Jerome P.; Hillygus, D. S. (2016). Bayesian latent pattern mixture models for handling attrition in panel studies with refreshment samples. Annals of Applied Statistics, 10(1), 118-143.
- Neuman, Heather B.; Schumacher, Jessica R.; Francescatti, Amanda B.; Adesoye, Taiwo; SB, Edge; ES, Burnside; DJ, Vanness; M, Yu; Si, Yajuan; D, McKellar; DP, Winchester; Greenberg, Caprice C. (2016). Utility of Clinical Breast Exams in Detecting Local-Regional Breast Events after Breast-Conservation in Women with a Personal History of High-risk Breast Cancer. Annals of surgical oncology, 23(10), 3385-3391.
- Early, D. M.; Berg, J. K.; Alicea, S.; Si, Yajuan; Aber, J. L.; Ryan, R. M.; Deci, E. L. (2016). The impact of every classroom, every day on high school student achievement: Results from a school-randomized trial. Journal of Research on Educational Effectiveness, 9, 3-29.
- Si, Yajuan; Pillai, Natesh S. and Gelman, Andrew (2015). Bayesian Nonparametric Weighted Sampling Inference. Bayesian Anal., 10(3), 605-625.
- Si, Yajuan; Reiter, Jerome P. and Hillygus, D. S. (2015). Semi-Parametric Selection Models for Potentially Non-Ignorable Attrition in Panel Studies with Refreshment Samples. Political Analysis, 23(1), 92-112.
- Makela, Susanna; Si, Yajuan and Gelman, Andrew (2014). Statistical Graphics for Survey Weights. Revista Colombiana De Estadística, 37(2), 285-295.
- Deng, Yiting; Hillygus, D. S.; Reiter, Jerome P.; Si, Yajuan and Zheng, Siyu (2013). Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples. Statistical Science, 28(2), 238-256.
- Si, Yajuan and Reiter, Jerome P. (2013). Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys. Journal of Educational and Behavioral Statistics, 38(5), 499-521.
- Si, Yajuan and Reiter, JP (2011). A Comparison of Posterior Simulation and Inference by Combining Rules for Multiple Imputation. Journal of Statistical Theory and Practice, 5, 335-347.