Current Research Interests

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









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

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:
GTwithBiomarker
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:
GTDilution
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]

Others
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:
LSMwithMemory
Disclaimer: The copyright on these papers belongs to the publisher of the journal. The papers may be downloaded for personal use only. Any other use requires prior permission of the author and the publisher.