Cheng Yong Tang

Profile Picture of Cheng Yong Tang

Cheng Yong Tang

  • Fox School of Business and Management

    • Statistics, Operations, and Data Science

      • Professor

      • Curtis Senior Research Fellow


Dr. Cheng Yong Tang is currently Associate Professor and the SeymourWolfbein Senior Research Fellow of Fox School of Business at Temple University. He is an Associate Editor for Reproducibility of Journal of the American Statistical Association, Application and Case Studies, an Associate Editor of Journal of Business and Economic Statistics. He served as the Director of the Graduate Programs in Statistics of the Department of Statistical Science in 2016-2019. Dr Tang is an Elected Member of the International Statistical Institute, a member of the American Statistical Association, a member of the Institute of Mathematical Statistics, and a member of the International Chinese Statistical Association.

Dr. Tang’s research is broad on data science and statistical methodology for solving practical problems. His current interests include empirical likelihood, longitudinal and dependent data analysis, high-dimensional inferences and nonparametric methods. Dr Tang’s research experience covers applied topics in data sciences, finance, econometrics, sampling survey statistics, statistical and machine learning.

Dr. Tang has published 30 research articles, with 18 of them in top econometrics and statistics journals, including the Journal of Econometrics, Annals of Statistics, Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society, Series B, and Annals of Applied Statistics. Dr Tang’s research has been funded by the NSF. He has been the sole PI of two NSF Grants, one on methods for longitudinal data analysis supported by the Division of Social and Economics Sciences, and the other on ensemble learning methods with random projections supported by the BIGDATA program.

Dr. Tang has been the recipient of numerous honors and awards. He received the distinguished 2019 ICSA President’s Citation Award. In the Fox School of Business and Management of Temple University, he has received awards including the Dean’s Research Honor Roll, Top 10 Highly Cited Faculty Members, and High Achievements in Sponsored Projects. He also received the National University of Singapore’s Young Scientist Award and Teaching Excellence Award, the IMS Laha Award, and Iowa State University’s Research Excellence and Teaching Excellence Awards.

Google Scholar: Google Scholar

Research Interests

  • Empirical likelihood
  • High-dimensional data analysis
  • Longitudinal and dependent data analysis
  • Financial statistics and Econometrics
  • Sampling statistics and analysis of missing data
  • Nonparametric and semiparametric statistical methods

Courses Taught




STAT 8001

Probability and Statistics Theory I


STAT 8002

Probability and Statistics Theory II


STAT 8101

Stochastic Processes


STAT 8102

High Dimensional Inference


Selected Publications


  • Chen, D., Li, C., Tang, C.Y., & Yan, J. (2024). The Leverage Effect Puzzle under Semi-nonparametric Stochastic Volatility Models. Journal of Business & Economic Statistics, 42(2), 548-562. Informa UK Limited. doi: 10.1080/07350015.2023.2203756.

  • Tang, C.Y. (2024). A model specification test for semiparametric nonignorable missing data modeling. Econometrics and Statistics, 30, 124-132. Elsevier BV. doi: 10.1016/j.ecosta.2021.08.005.

  • Duan, R., Liang, C.J., Shaw, P.A., Tang, C.Y., & Chen, Y. (2024). Testing the missing at random assumption in generalized linear models in the presence of instrumental variables. Scandinavian Journal of Statistics, 51(1), 334-354. Wiley. doi: 10.1111/sjos.12685.

  • Jing, N., Fang, E.X., & Tang, C.Y. (2023). Robust matrix estimations meet Frank–Wolfe algorithm. Machine Learning, 112(7), 2723-2760. Springer Science and Business Media LLC. doi: 10.1007/s10994-023-06325-w.

  • Weiping, Z., Yezhen, L., Yu, C., & Chengyong, T. (2023). Parsimonious Gaussian copula modelling through constrained Cholesky decomposition for data with temporal dependence. SCIENTIA SINICA Mathematica, 53(5), 777-777. Science China Press., Co. Ltd.. doi: 10.1360/scm-2022-0062.

  • Guo, X., Chen, Y., & Tang, C.Y. (2023). Information criteria for latent factor models: A study on factor pervasiveness and adaptivity. Journal of Econometrics, 233(1), 237-250. Elsevier BV. doi: 10.1016/j.jeconom.2022.03.005.

  • Sarkar, S.K. & Tang, C.Y. (2021). Adjusting the Benjamini-Hochberg method for controlling the false discovery rate in knockoff-assisted variable selection. Biometrika. doi: 10.1093/biomet/asab066.

  • Tong, P.F., Chen, S.X., & Tang, C.Y. (2021). Detecting and Evaluating Dust‐Events in North China With Ground Air Quality Data. Earth and Space Science, 9(1). doi: 10.1029/2021ea001849.

  • Yin, Z., Tong, J., Chen, Y., Hubbard, R.A., & Tang, C.Y. (2021). A cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data. J Am Med Inform Assoc, 29(1), 52-61. England. 10.1093/jamia/ocab222

  • Guo, X. & Tang, C. (2021). Specification tests for covariance structures in high-dimensional statistical models. Biometrika, 108(2), 335-351. Oxford University Press (OUP). doi: 10.1093/biomet/asaa073.

  • Chang, J., Chen, S., Tang, C., & Wu, T. (2021). High-dimensional empirical likelihood inference. Biometrika, 108(1), 127-147. doi: 10.1093/biomet/asaa051.

  • Bruce, S.A., Tang, C.Y., Hall, M.H., & Krafty, R.T. (2020). Empirical Frequency Band Analysis of Nonstationary Time Series. Journal of the American Statistical Association, 115(532), 1933-1945. Informa UK Limited. doi: 10.1080/01621459.2019.1671199.

  • Tang, C.Y. (2020). Precision Matrix Estimation by Inverse Principal Orthogonal Decomposition. Communications in Mathematical Research, 36(1), 68-92. doi: 10.4208/cmr.2020-0001.

  • Tang, C., Fang, E., & Dong, Y. (2020). High-dimensional interactions detection with sparse principal hessian matrix. Journal of Machine Learning Research, 21.

  • Tang, C., Zhang, W., & Leng, C. (2019). Discrete longitudinal data modeling with a mean-correlation regression approach. Statistica Sinica, 29(2), 853-876. doi: 10.5705/ss.202016.0435.