Faculty Profile

Yajuan  Si, PhD

Yajuan Si, PhD

Research Assistant Professor
Survey Research Center - Institute for Social Research
Department of Biostatistics

Rm 4014, ISR
426 Thompson St
Ann Arbor, MI 48104

Yajuan Si is a Research Assistant Professor in the Institute for Social Research and School of Public Health at the University of Michigan.  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.

  • PhD, Statistical Science, Duke University, 2012
  • M.S., Statistical Science, 2010
  • B.E., Statistics & Actuarial Science, Renmin University of China, 2008

Bayesian statistics, multilevel regression and poststratification, latent variable models, high-dimensional data, categorical data analysis, missing data imputation, complex survey inference, weighting approaches, causal inference and data confidentiality protection, with applications on epidemiogoly and health services research

  • Makela, S, Si, Y and Gelman, A (2018), Bayesian Inference under Cluster Sampling with Probability Proportional to Size, Statistics in Medicine, 37(26), 3849–3868
  • Si, Y, Reiter, JP and Hillygus, S (2016), Bayesian latent pattern mixture models in panel studies with refreshment samples, The Annals of Applied Statistics, 10(1), 118–143
  • Si, Y, Pillai, N and Gelman, A (2015), Bayesian nonparametric weighted sampling inference, Bayesian Analysis, 10(3), 605–625
  • Si, Y, Reiter, JP and Hillygus, S (2015), Semi-parametric selection models for potentially non- ignorable attrition in panel studies with refreshment samples, Political Analysis, 23, 92–112
  • Si, Y and Reiter, JP (2013), Nonparametric Bayesian multiple imputation for incomplete categorical variables in large-scale assessment surveys, Journal of Educational and Behavioral Statistics, 38, 499–521
  • Deng, Y, Hillygus, S, Reiter, JP, Si, Y and Zheng, S (2013), Handling attrition in longitudinal studies: The case for refreshment samples, Statistical Science, 22, 238–256
  • Si, Y and Reiter, JP (2011), A comparison of posterior simulation and inference by combining rules for multiple imputation, Journal of Statistical Theory and Practice, 5(2), 335–347

  • American Statistical Association
  • International Biometric Society-ENAR
  • International Society for Bayesian Analysis
  • Institute of Mathematical Statistics
  • International Chinese Statistical Association