Faculty Profile
Yajuan Si, PhD
- Research Associate Professor, Institute for Social Research
- Research Associate Professor, Biostatistics
Dr Si's research focuses on cutting-edge 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. Yajuan has established my research agenda on advancing survey
inference with Bayesian modeling techniques and adjusting for selection/nonresponse
bias in complex data modeling with various types of data (e.g., survey and big data)
and across broad substantive disciplines.
- PhD, Duke University, 2012
Research Interests:
- Bayesian statistics, survey inference, missing data analysis, confidentiality protection, causal inference
- Statistical adjustments of sample representation in community-level estimates of COVID-19 transmission and immunity
- Enhancing synthetic data techniques for practical applications
- Novel approaches to adjusting for population heterogeneity and representation in neuroimaging studies
- Multilevel regression and poststratification: A unified framework for survey weighted inference
Y Si, R Little, Y Mo, and N Sedransk (2022). A Case Study of Nonresponse Bias Analysis in Educational Assessment Surveys, Journal of Educational and Behavioral Statistics.
Y Si, S Heeringa, D Johnson, R Little, W Liu, F Pfeffer, and T Raghunathan (2022). Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics, Journal of Survey Statistics and Methodology.
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.
Y Si, S Heeringa, D Johnson, R Little, W Liu, F Pfeffer, and T Raghunathan (2022). Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics, Journal of Survey Statistics and Methodology.
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.