Pang Du (my Chinese name) Professor Department of Statistics Virginia Tech 409E Hutcheson Hall, Blacksburg, VA 24061 Phone: 540-231-5198 pangdu@vt.edu

I am a statistician interested in both statistical methodological research and statistical applications. My recent research interests in statistical methodology include functional data analysis, big data analytics, lifetime data analysis, and ROC curve methodology. I welcome collaborations from all disciplines and have collaborated with researchers from engineering, public health, computer science, and biology.
I joined Virginia Tech in 2006 upon obtaining my PhD from the Department of Statistics at Purdue University. My earlier alma maters are University of Science and Technology of China and Johns Hopkins University.

Here's a copy of my CV.

Research Interests

• Functional data analysis and nonparametric smoothing
  Types of data: data of curves/surfaces/shapes, long time series or collection of time series, image data, data generated from nonlinear processes, data of complex objects.
• High dimensional data and statistical learning
  Types of data: bioinformatics data, data of high dimensions, network data, data for classification or clustering.
• Lifetime data analysis and survival models
  Types of data: data on failure times or terminal events, censored data, data with limits of detection.
• Diagnostic test data and ROC curve methodology
  Types of data: data on medical diagnostic tests, industrial testing data.

• Jan 2024: Our paper, "Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities", got accepted by Statistics in Medicine. The paper introduces an optimal combintaion of multiple biomakers through the likelihood ratio of densties estimated by smoothing splines.
• Jan 2024: Our paper, "Multivariate functional clustering with variable selection and application to sensor data from engineering systems", got accepted by INFORMS Journal on Data Science. The paper introduces a new clustering method for multivariate functional data that can simultaneously select the important variables contributing to the clustering.
• Apr 2023: Our paper, "Contrast tests for groups of functional data", got accepted by Canadian Journal of Statistics. The paper introduces a new functional linear contrast test procedure for correlated functional data that can be used after a rejection of the functional ANOVA test.
• Feb 2023: Our paper, "Minimax Nonparametric Multi-sample Test under Smoothing", got accepted by Statistica Sinica. The paper proposes a new penalized likelihood ratio test for multi-sample testing that is minimax optimal.
• Aug 3, 2022: Quyen Do successfully completed her final defense and will join Corning, Inc. as a Statistical Engineer. Congratulations!
• May 2022: Our paper, "Shape Constrained Kernel PDF and PMF Estimation", got accepted by Statistica Sinica. The paper extends our estimation method for shape-constrained multivariate regression functions to the estimation of shape constrained PDFs and PMFs.

Selected Publications

• Du, Z., Du, P., and Liu, A. (2024). Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities. Statistics in Medicine, to appear.
• Xing, X., Shang, Z., Du, P., Ma, P., Zhong, W., and Liu, J. S. (2023). Minimax nonparametric multi-sample test under smoothing. Statistica Sinica, to appear.
• Du, P., Parmeter, C. F., and Racine, J. S. (2023). Shape constrained kernel PDF and PMF estimation. Statistica Sinica, to appear.
• Jin, H., Sun, X., and Du, P. (2023). Optimal function-on-function regression with interaction between functional predictors. Statistica Sinica 33(2), 1047-1068.
• Xu, Y., Du, P., Senger, R., Robertson, J., and Pirkle, J. (2021). ISREA: An efficient peak-preserving baseline correction algorithm for Raman spectra. Applied Spectroscopy 75(1): 34-45.
• Gao, Z., Du, P., Jin, R., and Robertson, J. L. (2020). Surface temperature monitoring in liver procurement via functional variance change point analysis, Annals of Applied Statistics 14(1), 143-159.
• Gao, Z., Shang, Z., Du, P., and Robertson, J. L. (2019). Variance change point detection under a smoothly-changing mean trend with application to liver procurement, Journal of the American Statistical Association 114(526), 773-781.
• Gao, Z., Shang, Z., Du, P., and Robertson, J. L. (2019). Variance change point detection under a smoothly-changing mean trend with application to liver procurement, Journal of the American Statistical Association 114(526), 773-781.
• Du, P., Sun, Z, Chen, H., Cho, J.-H., and Xu, S. (2018). Statistical estimation of malware detection metrics in the absence of ground truth, IEEE Transactions on Information Forensics and Security 13(12), 2965-2980.
• Chen, T. and Du, P. (2018). Promotion time cure rate model with nonparametric form of covariate effects, Statistics in Medicine 37(10), 1625-1635.
• Sun, X., Du, P., Wang, X., and Ma, P. (2018). Optimal penalized function-on-function regression under a reproducing kernel Hilbert space framework, Journal of the American Statistical Association 113(524), 1601-1611.
• Lian, H., Du, P., Li, Y., and Liang, H. (2014). Partially linear structure identification in generalized additive models with NP-dimensionality, Computational Statistics and Data Analysis 80, 197-208.
• Chen, Y., Du, P., and Wang, Y. (2014). Variable selection in linear models, WIREs Computational Statistics 6, 1-9.
  (a "hopefully" comprehensive review of variable selection for linear regression models)
• Du, P. and Wang, X. (2014). Penalized likelihood functional regression, Statistica Sinica 24(2), 1017-1041. (program available at the journal site)
• Wang, X., Du, P., and Shen, J. (2013). Smoothing splines with varying smoothing parameter, Biometrika 100(4), 955-970. (send us an email for the program used in the paper)
• Du, P., Parmeter, C. F., and Racine, J. S. (2013). Nonparametric kernel regression with multiple predictors and multiple shape constraints, Statistica Sinica 23(3), 1347-1371.
• Liang, H. and Du, P. (2012). Maximum likelihood estimation in logistic regression models with a diverging number of covariates, Electronic Journal of Statistics 6, 1838-1846.
• Du, P., Cheng, G., and Liang, H. (2012). Semiparametric regression models with additive nonparametric components and high dimensional parametric components, Computational Statistics and Data Analysis 56(6), 2006-2017.
• Wang, L., Du, P., and Liang, H. (2012). Two-component mixture cure rate model with spline estimated nonparametric components, Biometrics 68, 726-735.
• Ma, S. and Du, P. (2012). Variable selection in partly linear regression model with diverging dimensions for right censored data, Statistica Sinica 22, 1003-1020.
• Du, P., Jiang, Y., and Wang, Y. (2011). Smoothing spline ANOVA frailty model for recurrent event data, Biometrics 67, 1330-1339. li> Du, P. and Ma, S. (2010). Frailty model with spline estimated nonparametric hazard function, Statistica Sinica 20(2), 561-581.
• Du, P., Ma, S., and Liang, H. (2010). Penalized variable selection procedure for Cox models with semiparametric relative risk, Annals of Statistics 38, 2092-2117.
• Du, P. and Tang, L. (2009). Transformation-invariant and nonparametric monotone smooth estimation of ROC curves, Statistics in Medicine 28: 349-359.