Statistical Methodology

(† Current or previous Ph.D. student coauthors)

 

1.     W.-Z. Su† and X. Wang. (2022+) “Multiple Hypotheses Testing on Dependent Count Data with Covariate Effects”. Statistics and Its Interface. accepted. HMMtesting (Rpackage)

2.     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.  doi:10.1111/biom.13291

3.     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. doi: 10.1080/19466315.2020.1870546

4.     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. doi: 10.1080/02664763.2020.1725810.

5.     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. doi: 10.1177/0962280220950369

6.     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,  doi: 10.1080/00949655.2019.1710507

7.     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

8.     D. Li†, X. Wang, and D. K. Dey. (2019) “Power link functions in ordinal regression models with Gaussian process priors”, Environmetrics, accepted. doi: 10.1002/env.2564

9.     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, doi: 10.4310/19-SII563.

10.   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.

11.   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, in press.

12.   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, DOI: 10.1002/bimj.201500040.

13.   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, doi: 10.1007/s10584-016-1722-1.

14.   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, doi: 10.1002/sta4.98.

15.   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.

16.   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, doi: 10.1007/978-3-319-42571-9_10, in press.

17.   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, doi: 10.1007/s11002-015-9362-1.

18.   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.

19.   X. Wang and D. K. Dey. (2011). “Generalized extreme value regression for ordinal response data”. Environmental and Ecological Statistics 18(4), 619-634, doi: 10.1007/s10651-010-0154-8

  1. 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
  2. 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
  3. 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.     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). doi: 10.1016/j.scitotenv.2021.148033

2.     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.

  1. 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.
  2. 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), doi: 10.1002/pbc.26258.

5.     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., doi:10.1007/978-1-4939-6747-622.

  1. 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, doi:10.1002/jrsm.1209.
  2. 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, doi: 10.1007/s00429-015-1092-x. (PMID: 26246177)
  3. 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. doi: 10.1021/acs.jproteome.5b00859. (ACS Editors' Choice publication) Example (OVelos@65(A-J)) Fortran codes
  4. 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, doi: 10.1074/mcp.O115.051888.
  5. 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), doi:10.1038/sdata.2015.22.
  6. 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
  7. 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
  8. W. M. Dest, K. Guillard, S. L. Rackliffe, M-H Chen, and X. Wang. (2010). “Putting green speeds: a reality check!”, Applied Turfgrass Science, doi:10.1094/ATS-2010-0216-01-RS.

 

Doctoral Students Supervision

1.     Leo L. Duan.  08/2015 (joint with Rhonda Szczesniak, CCHMC)

            Dissertation: Bayesian Nonparametric Methods with Applications in Longitudinal, Heterogeneous and Spatiotemporal Data

 

2.     Dan Li.  08/2016.

Dissertation: Bayesian Nonparametric and Semiparametric Models for Categorical, Survival and Longitudinal Data

 

3.     Yue Zhang.  08/2016 (joint with Bin Zhang, CCHMC)

Dissertation: Bayesian Cox Models for Interval-Censored Survival Data

 

4.     Weiji Su.  08/2020 (joint with Rhonda Szczesniak, CCHMC)

Dissertation: Flexible Joint Hierarchical Gaussian Process Model for Longitudinal and Recurrent Event Data

 

5.     Weizhe Su. 12/2020

Dissertation: Bayesian Hidden Markov Model in Multiple Testing on Dependent Count Data

 

Undergraduate Research Supervision

            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

Deep Learning on Loneliness, Stress, and Technology Usage during the COVID-19 Pandemic