author* indicates the corresponding author
Wang, L.* (2025+). Balanced Subsampling for Robust Regression with Categorical Predictors. Statistica Sinica, DOI: 10.5705/ss.202023.0434.
Zhang, X., Lin, D. K. J., and Wang, L.* (2025). Digital Twin Optimization: A Sequential Approach. Computers & Industrial Engineering, to appear.
Kosko, M., Wang, L., and Santacatterina, M. (2024). A Fast Bootstrap Algorithm for Causal Inference with Large Real-world Data. Statistics in Medicine, 43(15), 2894–2927.
Xi, N.M., Ji, HL. and Wang, L.* (2024). Understanding Sarcoidosis Using Large Language Models and Social Media Data. Journal of Healthcare Informatics Research, 1-26.
Zhang, Y., Wang, L.*, Zhang, X., and Wang, H., (2024). Independence-Encouraging Subsampling for Nonparametric Additive Models. Journal of Computational and Graphical Statistics, 33(4), 1424-1433.
Zhu, J., Wang, L., and Sun, F. (2024). Group-Orthogonal Subsampling for Big Data Linear Mixed Models. Journal of Computational and Graphical Statistics, 33(3), 1037-1046.
Denardo, S. J., Vlachos, P. P., Meyers, B. A., Babakhani-Galangashi, R., Wang, L., Gao, Z., and Tcheng, J. E. (2024). Translating proof-of-concept for platelet slip into improved antithrombotic therapeutic regimens. Platelets, 35(1), 2353582.
Yin, Y., Wang, L.*, and Xu, H. (2023). Construction of Maximin L1-distance Latin hypercube designs. Electronic Journal of Statistics,17(2), 3942-3968.
Zhang, X., Lin, D. K. J., and Wang, L.* (2023). Digital triplet: a sequential methodology for digital twin learning. Mathematics, 11, 2661. [PDF]
Wang, L.*, Xu, H., and Liu, M.-Q. (2023). Fractional Factorial Designs for Fourier Cosine Models. Metrika, 86: 373-390. [PDF]
Xi, N.M., Wang, L., and Yang, C. (2022). Improving the Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data. Scientific Reports, 12, 11143. [Code] [Data]
Song, D., Xi, N.M., Li, J.J.*, and Wang, L.* (2022). scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data. Bioinformatics 38(11), 3126. [PDF] [Python Package] [R Package] [Supplement]
Hossen, I., Anders, M.A., Wang, L., Adam, G.C. (2022). Data-driven RRAM device models using Kriging interpolation. Scientific Reports, 12, 5963. [PDF]
Wang, L.* and Xu, H. (2022). A Class of Multilevel Nonregular Designs for Studying Quantitative Factors. Statistica Sinica, 32, 825-845. [PDF]
Hossen, I., Zhang, Y., Anders, M. A., Wang, L., and Adam, G. C. (2022). Heteroscedastic Gaussian Process Regression for ReRAM Device Modeling. The 6th IEEE Electron Devices Technology and Manufacturing (EDTM) Conference.
Wang, L.*, Elmstedt, J., Wong, W. K., and Xu, H. (2021). Orthogonal Subsampling for Big Data Linear Regression. Annals of Applied Statistics,15(3): 1273-1290. [PDF] [Supplemental Content] [C++ code]
Xiao, Q., Wang, L., and Xu, H. (2019). Application of Kriging Models for a Drug Combination Experiment on Lung Cancer. Statistics in Medicine, 38, 236-246.
Wang, L., Xiao, Q., and Xu, H. (2018). Optimal Maximin L1-distance Latin Hypercube Designs Based on Good Lattice Point Designs. Annals of Statistics, 46, 3741-3766. [PDF]
Wang, L., Sun, F., Lin, D. K. J., and Liu, M.-Q. (2018). Construction of Orthogonal Symmetric Latin Hypercube Designs. Statistica Sinica, 28, 1503-1520. [PDF]
Wang, L., Yang, J.-F., Lin, D. K. J., and Liu, M.-Q. (2015). Nearly Orthogonal Latin Hypercube Designs for Many Design Columns. Statistica Sinica, 25, 1599-1612. [PDF] [Supplementary Material]