Current Research Interests

  • Group testing
  • Pooled biomarker data analysis
  • Order-restricted inference
  • Nonparametric and semiparametric regression
  • Shrinkage methods
  • Quantile regression

Acknowledgement: research is supported by NIH (
R03 AI135614, R21 AG070659) and USC ASPIRE I.

Pooled Biomonitoring
Wang, D., Mou, X.*, and Liu, Y. Varying coefficient regression analysis for pooled biomonitoring data. Biometrics, in press. [pdf] [doi]
Wang, D., Mou, X.*, Li, X., and Huang, X. (2020). Local polynomial regression for pooled response data. Journal of Nonparametric Statistics 32, 814-837. [pdf] [doi]
Lin, J.* and Wang, D. (2018). Single-index regression analysis of pooled biomarker assessments. Journal of Nonparametric Statistics 30, 813-833. [pdf] [doi]
R code: SiMPool

Group Testing
Hou, P.*, Tebbs, J. , Wang, D., McMahan, C., and Bilder, C. (2020). Array testing with multiplex assays. Biostatistics 21, 417-431. [pdf] [doi]
An R Shiny App + R code: GitHub_Link
Lin, J.*, Wang, D., and Zheng, Q. (2019). Regression analysis and variable selection for two-stage multiple-infection group testing data. Statistics in Medicine 38, 4519-4533. [pdf] [doi]
Gregory, K., Wang, D., and McMahan, C. (2018). Adaptive elastic net for group testing data. Biometrics 75, 13-23. [pdf] [doi]
R code: GitHub Link
Wang, D., McMahan, C., Tebbs, J., and Bilder, C. (2018). Group testing case identification with biomarker information. Computational Statistics and Data Analysis 122, 156-166. [pdf] [doi]
R code:
Wang, D., McMahan, C., and Gallagher, C. (2015). A general parametric regression framework for group testing data with dilution effects. Statistics in Medicine 34, 3606-3621. [pdf] [doi]
R code:
Wang, D., McMahan, C., Gallagher, C., and Kulasekera, K. (2014). Semiparametric group testing regression models. Biometrika 101, 587-598. [pdf] [doi]
Wang, D., Zhou, H., and Kulasekera, K. (2013). A semi-local likelihood regression estimator of the proportion based on group testing data. Journal of Nonparametric Statistics 25, 209-221. [pdf] [doi]
Order-restricted inference
Wang, D. and Tang, C.* (2021). Testing against uniform stochastic ordering with paired observations. Bernoulli 27, 2556-2563. [pdf] [doi]
Tang, C.*, Wang, D., El Barmi, H., and Tebbs, J. (2021). Testing for positive quadrant dependence. American Statistician 75, 23-30. [pdf] [doi]
R code: GitHub Link
Wang, D., Tang, C.*, and Tebbs, J. (2020). More powerful goodness-of-fit tests for uniform stochastic ordering. Computational Statistics and Data Analysis 144, 106898. [pdf] [doi]
R Package: TestUSO
Tang, C.*, Wang, D., and Tebbs, J. (2017). Nonparametric goodness-of-fit tests for uniform stochastic ordering. Annals of Statistics 48, 2565-2589. [pdf] [doi]
R Package: TestUSO

Shinkage Methods
Russell, B., Wang, D., and McMahan, C. (2017). Spatially modeling the effects of meteorological drivers of PM2.5 in the eastern United States via a local linear penalized quantile regression estimator. Environmetrics 28, 1-16. [pdf] [doi]
Wang, D. and Kulasekera, K. (2012). Parametric component detection and variable selection in varying-coefficient partially linear models. Journal of Multivariate Analysis 112, 117-129. [pdf] [doi]

Wang, D., Jiang, C., and Park, C. (2019). Reliability analysis of load-sharing systems with memory. Lifetime Data Analysis 25, 341-360. [pdf] [doi]
R code:
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