Current Research Interests
Pooled Biomonitoring
Group Testing
Order-restricted Inference
Nonparametric and Semiparametric Regression
Shrinkage Methods
Quantile Regression
Publications
Acknowledgment: research is supported by NIH (R03 AI135614, R21 AG070659) and USC ASPIRE I.
*Advisee
2024
Tang, C. and Wang, D. (2024). Multiple ordinal dominance curves and uniform stochastic ordering. Statistica Sinica, in press. [R code: MSUSO]
Li, Y.*, Wang, D., and Tebbs, J. (2024+). A group testing based exploration of age-varying factors in chlamydia infections among Iowa residents. Biometrics, under review.
2023
Mou, X. and Wang, D. (2024). Additive partially linear model for pooled biomonitoring data. Computational Statistics and Data Analysis 190, 107862. [Rcode: APLMforPool]
Liu, Y., Wang, D., Li, L., and Li, D. (2023). Assessing disparities in Americans' exposure to PCBs and PBDEs based on NHANES pooled biomonitoring data. Journal of American Statistical Association 118, 1528-1550. [pdf, supp]
2022
Wang, D., Mou, X.*, and Liu, Y. (2022). Varying coefficient regression analysis for pooled biomonitoring data. Biometrics 78, 1328-1341. [R code: VCMforPB]
Cao, X.* and Gregory, K., and Wang, D. (2022). Inference for sparse linear regression based on the leave-one-covariate-out solution path. Communications in Statistics–Theory and Methods 52, 6640-6657.
2020
Wang, D., Mou, X.*, Li, X., and Huang, X. (2020). Local polynomial regression for pooled response data. Journal of Nonparametric Statistics 32, 814-837. [R code: LPRforPool]
Hou, P.*, Tebbs, J. , Wang, D., McMahan, C., and Bilder, C. (2020). Array testing with multiplex assays. Biostatistics 21, 417-431. [R code: Multiplex][R Shiny App: MultiGTSiM]
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. [R code: ImprovedGOFforUSO]
2019
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.
Wang, D., Jiang, C., and Park, C. (2019). Reliability analysis of load-sharing systems with memory. Lifetime Data Analysis 25, 341-360. [R code: LSMwMemory]
2018
Lin, J.* and Wang, D. (2018). Single-index regression analysis of pooled biomarker assessments. Journal of Nonparametric Statistics 30, 813-833. [R code: SimPool]
Gregory, K., Wang, D., and McMahan, C. (2018). Adaptive elastic net for group testing data. Biometrics 75, 13-23. [R code: aenetget]
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. [R code: GTwBiomarker]
2017
Tang, C.*, Wang, D., and Tebbs, J. (2017). Nonparametric goodness-of-fit tests for uniform stochastic ordering. Annals of Statistics 48, 2565-2589. [R package: TestUSO]
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.
Before 2016
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. [R code: GTDilution]
Wang, D., McMahan, C., Gallagher, C., and Kulasekera, K. (2014). Semiparametric group testing regression models. Biometrika 101, 587-598.
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.
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.