Yajuan Si

Photo of Dr. Yajuan Si
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Yajuan Si is a Research Associate 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.

Research Interests
Statistical methodology linking design- and model-based approaches for survey inference, missing data analysis, confidentiality protection involving the creation and analysis of synthetic datasets, and causal inference with observational data, with interdisciplinary collaborators to improve the application of statistics in health and social sciences.

Selected Publications

Y Si⋆, B West, P Veliz, M Patrick, J Schulenberg, D Kloska, Y Terry-McElrath, and S Mc- Cabe (2022). An Empirical Evaluation of Alternative Approaches to Adjusting for Attrition When Analyzing Longitudinal Survey Data on Young Adults’ Substance Use Trajectories, International Journal of Methods in Psychiatric Research

Y Si, L Covello, S Wang, T Covello, and A Gelman (2022). Beyond Vaccination Rates: A Synthetic Random Proxy Metric of Total SARS-CoV-2 Immunity Seroprevalence in the Community, Epidemiology, 33(4), 457–464

Y Si  and P Zhou (2021). Bayes-raking: Bayesian Finite Population Inference with Known Margins, Journal of Survey Statistics and Methodology, 9(4), 833–855

Y Si, M Palta, and M Smith (2020). Bayesian Profiling Multiple Imputation for Missing Hemoglobin Values in Electronic Health Records, Annals of Applied Statistics 14(4), 1903–1924

Y Si, R Trangucci, J Gabry, and A Gelman (2020). Bayesian Hierarchical Weighting Adjustment and Survey Inference, Survey Methodology, 46(2), 181–214

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

NIH/NIMHD: Statistical Adjustments of Sample Representation in Community-level Estimates of COVID-19 Transmission and Immunity

NIH/NICHD: Novel Approaches to Adjusting for Population Heterogeneity and Representation in Neuroimaging Studies

NIH/NIDA: The Healthy Brain and Child Development National Consortium Administrative Core

NISS/NCES: Nonresponse Bias Adjustment for Large-scale Education Surveys

USDA: Methodological Research on Mobile Technology in the Collection of Household Food Expenditure Data

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