Associate Professor, Statistics;
A&S Mathematical Sciences - 0025
For current research, please visit
Ph.D., University of Connecticut 2009 (Statistics)
Ph.D., University of Connecticut 2007 (Economics)
2017 - - present, Associate Professor, University of Cincinnati, OH
2011 -2017 Assistant Professor, University of Cincinnati, OH
2009 -2011 Postdoctoral Fellow, National Institute of Statistical Sciences (NISS), NC
Bayesian methodology and computation; Categorical data analysis; Scalable modeling of complex, high-dimensional data; Applications of statistical models in genomics and proteomics data.
Grant: #R01MH119814 Investigators:Herman, James; Song, Seongho; Ulrich-Lai, Yvonne; Wang, Xia 09-11-2019 -06-30-2024 National Institute of Mental Health Stress resilience by natural rewards: neurocircuit mechanisms Role:Collaborator $561,667.00 Awarded Level:Federal
Peer Reviewed Publications
W.-Z. Su* and X. Wang. (2020). “Hidden Markov model in multiple testing on dependent count data”. Journal of Statistical Computation and Simulation, accepted. DOI: 10.1080/00949655.2019.1710507 (* PhD student coauthor)
X. Wang, A. Shojaie, and J. Zou. (2019) “Bayesian hidden Markov models for dependent large-scale multiple testing”, Computational Statistics & Data Analysis (136), 123-136. DOI: 10.1016/j.csda.2019.01.009.
Y. Zhang*, X. Wang, and B. Zhang. (2019) “Bayesian approach for clustered interval-censored data with time-varying covariate effects”, Statistics and Its Interface 12(3), 457--465.. (*Ph.D. student coauthor)
D. Li, X. Wang, and D. K. Dey. (2019) “Power Link Functions in Modeling Dependent Ordinal Data”, Environmetrics 30(6), https://doi.org/10.1002/env.2564
L.L. Duan, X. Wang, J.P. Clancy, and R. D. Szczesniak. (2018). “Joint hierarchical Gaussian process model with application to personalized prediction in medical monitoring”, Stat 7(1), doi: 10.1002/sta4.178
L. L. Duan, R. D. Szczesniak, and X. Wang. (2017). “Functional inverted-Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data”, Environmetrics 28 (7), doi:10.1002/env.2467.
D. Li *, X. Wang, & D. K. Dey. (2016). “A flexible cure rate model for spatially correlated survival data based on generalized extreme value distribution and Gaussian process priors,” Biometrical Journal 58(5), 1178–1197, doi: 10.1002/bimj.201500040. (* PhD student coauthor)
X. Wang, M-H Chen, R. C. Kuo, and D. K. Dey. (2015). “Bayesian spatial-temporal modeling of ecological zero-inflated count data,” Statistica Sinica 25 (1), 189-204.
D. Li*, X. Wang, L. Lin, & D. K. Dey. (2015). “Flexible link functions in nonparametric binary regression with Gaussian process priors,'' Biometrics 72, 707–719, doi: 10.1111/biom.12462. (* PhD student coauthor).
Categorical Data Analysis