Yajuan Si, PhD

Biography

Yajuan Si obtained a PhD degree in Statistics in the Department of Statistical Science at Duke University in 2012. She then became a postdoctoral research scholar in statistics at Columbia University. Yajuan joined the University of Wisconsin-Madison in 2014. Dr Si’s research lies in cutting-edge methodology development in streams of Bayesian computation, latent variable models, complex survey inference, missing data, causal inference, and data confidentiality. Currently she is working on hierarchical Bayesian approaches to build a unified framework in sample weighting inferences with particular emphasis in multilevel regression and poststratification, weight smoothing and trimming, deep interactions, borrowing auxiliary information and complex sampling schemes. This approach also provides a path toward the newly emerging big data explosion problem. Yajuan formalizes nonparametric Bayesian multiple imputation framework to deal with panel data of high dimensions and complex dependency structures, which offers novel strategies for flexible modeling and efficient computation to handle categorical data and non-ignorable attrition.

Affiliations/Associations

• American Statistical Association
• International Society for Bayesian Analysis
• Institute of Mathematical Statistics

Recent Honors/Awards

• NSF Travel Award, 2014, Frontiers of Hierarchical Modeling in Observational Studies, Complex Surveys and Big Data

Select Publications

Si Y, Reiter JP and Hillygus S. Bayesian Latent Pattern Mixture Models in Panel Studies With Refreshment Samples. The Annals of Applied Statistics 2016; 10(1): 118-143.

Early D, Berg J, Alicea S, Si Y, Aber J, Ryan R and Deci E. The Impact of Every Classroom, Every Day on Student Achievement: Results From a School-Randomized Efficacy Trial. Journal of Research on Educational Effectiveness 2016; 9(1): 3-29.

Si Y, Pillai N and Gelman A. Bayesian Nonparametric Weighted Sampling Inference. Bayesian Analysis 2015; 10(3): 605-625.

Si Y, Reiter JP, Hillygus S. Semi-Parametric Selection Models for Potentially Nonignorable Attrition in Panel Studies With Refreshment Samples. Political Analysis 2015; 23: 92-112.

Makela S, Si Y, Gelman A. Statistical Graphics for Survey Weights. Journal of Revista Columbiana De Estadistica 2014; 37: 285-295.

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, Zheng S. Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples. Statistical Science 2013; 22: 238-256.

Si Y, Reiter JP. A Comparison of Posterior Simulation and Inference by Combining Rules for Multiple Imputation. Journal of Statistical Theory and Practice 2011; 5: 335-347.