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.
Si, Y., Reiter, J. P. 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, J. P. 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, J.P. (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, J.P., Si, Y. and Zheng, S. (2013), Handling attrition in longitudinal studies: The case for refreshment samples, Statistical Science, 22, 238 - 256.