Statistical Methodology

† PhD student coauthor.

1. E. Odoom† and X. Wang. (2026+). “Consistent and Scalable Variable Selection with Robust Link Functions”. The New England Journal of Statistics in Data Science. accepted.
2. W.-J. Su† X. Wang, P. M. Afonso, E.-R. Andrinopoulou, R. D. Szczesniak. (2026). “Estimating Disease Progression in the Presence of an Outcome-dependent Visiting Process with Application to Cystic Fibrosis Clinical Data”, The New England Journal of Statistics in Data Sciences. accepted.
3. E. Odoom†, J. R. Ouyang, X. Cao and X. Wang. (2026) “Hierarchical consistent skinny Gibbs sampler in logistic regression using Pólya–Gamma latent variables”. Statistics and Its Interface 19(2). 179 - 196.
4. H-C. Zhang† and X. Wang. (2026). “Variable Selection for Zero-Inflated Poisson Regression Model”. Journal of Statistical Computation and Simulation 96(4), 946-963. doi: 10.1080/00949655.2025.2575872
5. W.-Z. Su † and X. Wang. (2023) “Multiple Hypotheses Testing on Dependent Count Data with Covariate Effects”. Statistics and Its Interface 16 (3). 409 – 417. HMMtesting (Rpackage )
6. W.-J. Su †, X. Wang and R. D. Szczesniak. (2021). “ Flexible link functions in a joint hierarchical Gaussian process model”, Biometrics 77(2), 754-764. 10.1111/biom.13291
7. W.-J. Su†, E. Gecili, X. Wang and R. D. Szczesniak. (2021) “ An empirical comparison of segment and stochastic linear mixed effects models to estimate rapid disease progression in longitudinal biomarker studies”. Statistics in Biopharmaceutical Research 13(3), 270-279. 10.1080/19466315.2020.1870546
8. X. Wang, J. Pancras, and D. K. Dey. (2021). “ Investigating emergent nested geographic structure in consumer purchases: a Bayesian dynamic multi-scale spatiotemporal modeling approach”, Journal of Applied Statistics 48(3), 410-433. 10.1080/02664763.2020.1725810.
9. W.-J. Su †, X. Wang and R. D. Szczesniak. (2020). “ Risk factor identification in cystic fibrosis by flexible hierarchical joint models''. Statistical Methods in Medical Research 30 (1), 244-260. 10.1177/0962280220950369
10. W.-Z. Su † and X. Wang. (2020). “ Hidden Markov model in multiple testing on dependent count data”, Journal of Statistical Computation and Simulation 90(5), 889-906, 10.1080/00949655.2019.1710507
11. 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,
12. D. Li†, X. Wang, and D. K. Dey. (2019) “ Power link functions in ordinal regression models with Gaussian process priors”, Environmetrics, accepted. 10.1002/env.2564
13. 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, 10.4310/19-SII563.
14. 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), 10.1002/sta4.178.
15. 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), 10.1002/env.2467.
16. D. Li†, X. Wang, and 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, 10.1002/bimj.201500040.
17. E. Salazar, D. Hammerling, X. Wang, B. Sansó, A.O. Finley, and L. Mearns. (2016). “Observation-based blended projections from ensembles of regional climate models,'' Climatic Change 138(1), 55-69, 10.1007/s10584-016-1722-1.
18. D. Li†, X. Wang, S. Song, N. Zhang, and D. K. Dey. (2015). “ Flexible link functions in a joint model of binary and longitudinal data”, Stat 4(1), 320–330, 10.1002/sta4.98.
19. 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, 10.1111/biom.12462.
20. X. Wang, M-H Chen, R. C. Kuo, and D. K. Dey. (2015). “Dynamic spatial pattern recognition in count data,” Z. Jin et al. (eds.), New Developments in Statistical Modeling, Inference and Application, ICSA Book Series in Statistics, Springer International Publishing Switzerland, 10.1007/978-3-319-42571-9_10.
21. J. Pancras, X. Wang, and D. K. Dey. (2015). “ Investigating the impact of customer stochasticity on firm price discrimination strategies using a new Bayesian mixture scale heterogeneity model,” Marketing Letters 27(3), 537-552, 10.1007/s11002-015-9362-1.
22. 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, 189-204.
23. X. Wang and D. K. Dey. (2011). “ Generalized extreme value regression for ordinal response data”. Environmental and Ecological Statistics 18(4), 619-634, 10.1007/s10651-010-0154-8
24. E. Salazar, B. Sansó, A. Finley, D. Hammerling, I. Steinsland, X. Wang & P. Delamater. (2011). “ Comparing and blending regional climate model predictions for the American southwest”. Journal of Agricultural, Biological, and Environmental Statistics, Special Issue: Computer Models and Spatial Statistics for Environmental Science 16(4), 586-605
25. X. Wang and D. K. Dey. (2010). “ Generalized extreme value regression for binary response data: an application to B2B electronic payments system adoption”. Annals of Applied Statistics, 4 (4), 2000-2023
26. X. Wang, D. K. Dey and S. Banerjee. (2010). “Non-Gaussian hierarchical generalized linear geostatistical model selection”. In M-H Chen, D. K. Dey, P. Müller, D. Sun, and K. Ye (Eds.), Frontiers of Statistical Decision Making and Bayesian Analysis (pp. 484-496). Springer.

Interdisciplinary Research

1. X. Wang, D. Washington and G. F. Weber. (2021). “Complex systems analysis informs on the spread of COVID-19”. Epidemiologic Methods 10 (s1), pp. 20210019. doi: 10.1515/em-2021-0019.
2. X. Wang and G. F. Weber. (2021). “Quantitative analysis of protein evolution: the phylogeny of Osteopontin”. Frontiers in Genetics 12:700789.doi: 10.3389/fgene.2021.700789.
3. M. T. Booth, M. Urbanic, X. Wang, and J.J. Beaulieu. (2021). “Bioturbation frequency alters methane emissions from reservoir sediments”, Science of the Total Environment 789(1). 10.1016/j.scitotenv.2021.148033
4. A.E. Egan, A.M.K. Thompson, D. Buesing, S. M. Fourman, A.E.B. Packard, T. Terefe1, D. Li †, X. Wang, S. Song, M.B. Solomon, Y.M. Ulrich-Lai. (2018) “Palatable food affects HPA axis responsivity and forebrain neurocircuitry in an estrous cycle-specific manner in female rats”, Neuroscience 384(1), 224-240.
5. V. N Chihota, A. Niehaus, E. M. Streicher, X. Wang, S. L. Sampson, P. Mason, G. Källenius, S. G. Mfinanga, M. Pillay, M. Klopper, W. Kasongo, M. Behr, N. C. Gey van Pittius, P. van Helden, D. Couvin, N. Rastogi, R. M. Warren.(2018). “Geospatial distribution of Mycobacterium tuberculosis genotypes in Africa”. PLoS ONE 13(8): e0200632.
6. K. Dorris, C. Liu, D. Li, T. Hummel, X. Wang, J. Perentesis, M-O Kim, M. Fouladi. (2017) “A comparison of safety and efficacy of cytotoxic versus molecularly-targeted drugs in pediatric phase I solid tumor oncology trials,” Pediatric Blood & Cancer 64(3), 10.1002/pbc.26258.
7. X. Wang. (2017). “Statistical Assessment of QC Metrics on Raw LC-MS/MS Data”, Methods in Molecular Biology, Proteomics: Methods and Protocols, 325–337. L. Comai, J. Katz, and P. Mallick (editors), Springer Nature, New York., 10.1007/978-1-4939-6747-622.
8. M-O. Kim, X. Wang, C. Liu, K. Dorris, M. Fouladi, and S. Song. (2016). “ Random-effects meta-analysis for systematic reviews of Phase I clinical trials: rare events and missing data,” Research Synthesis Methods, 10.1002/jrsm.1209.
9. Y. M. Ulrich-Lai, A. M. Christiansen, X. Wang, S. Song, and J.P. Herman. (2016). “ Statistical modeling implicates neuroanatomical circuit mediating stress relief by 'comfort' food,” Brain Structure and Function 221(6), 3141-3156, 10.1007/s00429-015-1092-x. (PMID: 26246177)
10. D. L. Tabb, X. Wang, S. A. Carr, et al. (2015). “ Reproducibility of differential proteomic technologies in CPTAC fractionated xenografts,” Journal of Proteome Research, 15(3), 691--706. 10.1021/acs.jproteome.5b00859. ( ACS Editors' Choice publication ) Example (OVelos@65(A-J)) Fortran codes
11. K.L. Bennett, X. Wang, C.E. Bystrom, et al. (2015) “ The 2012 ABRF Proteomic Research Group Study: assessing longitudinal intra-laboratory variability in routine peptide liquid chromatography tandem mass spectrometry analyses,” Molecular & Cellular Proteomics 14 (12), 3299-3309, 10.1074/mcp.O115.051888.
12. R. Slebos, X. Wang, X-J. Wang, B. Zhang, D. L. Tabb, D. Liebler. (2015). “ Proteomic analysis of colon and rectal carcinoma using standard and customized databases,” Scientific Data 2, Article number: 150022 (2015), 10.1038/sdata.2015.22.
13. X. Wang, M. C. Chambers, L. J. Vega- Montoto, D. M. Bunk, S. E. Stein, D. L. Tabb. (2014). “ QC metrics from CPTAC raw LC-MS/MS data interpreted through multivariate statistics,” Analytical Chemistry 86(5), 2497-2509. R codes
14. P. A. Rudnick (*), X. Wang (*), E. Yan, N. Sedransk and S. E. Stein. (2014). “ Improved normalization of systematic biases affecting ion current measurements in label-free proteomics data,” Molecular & Cellular Proteomics 13(5), 1341-1351. (* These authors contributed equally to this work.) R codes
15. W. M. Dest, K. Guillard, S. L. Rackliffe, M-H Chen, and X. Wang. (2010). “ Putting green speeds: a reality check!”, Applied Turfgrass Science, 10.1094/ATS-2010-0216-01-RS.

Student Supervision

Doctoral Students

• Leo L. Duan. 08/2015 (joint with Rhonda Szczesniak, CCHMC)
  Dissertation: Bayesian Nonparametric Methods with Applications in Longitudinal, Heterogeneous and Spatiotemporal Data
• Dan Li. 08/2016.
  Dissertation: Bayesian Nonparametric and Semiparametric Models for Categorical, Survival and Longitudinal Data
• Yue Zhang. 08/2016 (joint with Bin Zhang, CCHMC)
  Dissertation: Bayesian Cox Models for Interval-Censored Survival Data
• Weiji Su. 08/2020 (joint with Rhonda Szczesniak, CCHMC)
  Dissertation: Flexible Joint Hierarchical Gaussian Process Model for Longitudinal and Recurrent Event Data
• Weizhe Su. 12/2020
  Dissertation: Bayesian Hidden Markov Model in Multiple Testing on Dependent Count Data
• Haichao Zhang. 12/2024.
  Dissertation: Bayesian Modeling and Variable Selection in Dependent Zero-Inflated Count Data.

Undergraduate Research

• Yiren Wang. 02/2014 – 08/2015. Taft Undergraduate Research Awardee.
  Change point detection in statistical process control
• Khoa Huynh Le Anh. 2019–2020. Undergraduate capstone project.
  LASSO in generalized linear regression model
• My Nguyen. 2020.
  Gaussian Process in Machine Learning
• Behruz Bazarov. 2022. Taft Undergraduate Summer Research Award
  Deep Learning on Loneliness, Stress, and Technology Usage during the COVID-19 Pandemic