Yajuan Si is a Research Assistant Professor in the Survey and Data Science 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.
Statistical methodology development on Bayesian statistics, survey inference, missing data imputation, causal inference and data confidentiality protection, with applications on public health and social sciences.
Si Y, Reiter JP, Hillygus S. Bayesian latent pattern mixture models for handling attrition in panel studies with refreshment samples. The Annals of Applied Statistics 2016; 10:118-143.
Si Y, Pillai N, Gelman A. Bayesian nonparametric weighted sampling inference. Bayesian Analysis 2015; 10(3): 605-625.
Si Y, Hillygus D, Reiter JP. Semi-parametric selection models for potentially non-ignorable attrition in panel studies with refreshment samples. Political Analysis 2015; 23:92-112.
Si Y, Reiter JP. Nonparametric Bayesian multiple imputation for incomplete categorical variables in large-scale assessment surveys. Journal of Educational and Behavioral Statistics 2013; 38:499-521.
Deng, Y, Hillygus, S, Reiter, JP, Si, Y and Zheng. Handling attrition in longitudinal studies: The case for refreshment samples. Statistical Science 2013; 22, 238-256.;
Current Research Projects
NSF/MMS: Multilevel Regression and Poststratification: A Unified Framework for Survey Weighted Inference
NIH/NIDDK: Profiling Missing Data in Electronic Health Records for Diabetes Care Research
NIH/NIDDK: Statistical Methods for Healthcare in Complex Patients with Diabetes
U.S. Department of Agriculture: Methodological Research on Mobile Technology in the Collection of Household Food Expenditure Data