Biography
Dr. Cheng Yong Tang is Associate Professor of Statistics and the Director of the Graduate Programs in Statistics of the Department of Statistical Science, Fox School of Business at Temple University. Dr. Tang is the Seymour Wolfbein Senior Research Fellow of the Fox School of Business and Management of Temple University.
He earned his PhD in Statistics in 2008 from Iowa State University. Prior to joining Temple University in 2014, he worked as Assistant and Associate Professor with tenure in the Department of Statistics and Applied Probability of National University of Singapore, 2008-2013, and as Assistant Professor of Business Analytics in the Business School of University of Colorado at Denver, 2012-2014.
Dr. Tang has published 29 research articles, with 17 of them in the top statistics and econometrics journals (JVC A and A-* levels) including the Annals of Statistics, Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society, Series B, Journal of Econometrics, and Annals of Applied Statistics.
Dr. Tangs research is on real data oriented statistical methodology for solving practical problems, including high-dimensional statistical methods, empirical likelihood and nonparametric methods. Dr Tang’s research experience covers topics in data sciences, finance, econometrics, sampling survey statistics, and statistical learning.
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. He has also been the PI of a Subaward from an NIH R01 Grant.
Dr. Tang has also been the recipient of multiple honors over multiple years in the Fox School of Business and Management of Temple University, including the Deans Research Honor Roll, Top 10 Highly Cited Faculty Members, and High Achievements in Sponsored Projects of the Fox School of Business. He is the recipient of the National University of Singapores Young Scientist Award and Teaching Excellence Award, the IMS Laha Award, as well as Iowa State Universitys Research Excellence and Teaching Excellence Awards.
Dr Tang is an Elected Member of the International Statistical Institute, a Fellow of the Royal Statistical Society, a member of the American Statistical Association, and a member of the Institute of Mathematical Statistics.
Google Scholar: Google Scholar
Research Interests
- Statistical methodology: High-dimensional data analysis Empirical likelihood Longitudinal data analysis Financial statistics and Econometrics Sampling statistics and analysis of missing data Nonparametric and semiparametric statistical methods
Courses Taught
Number | Name | Level |
|---|---|---|
STAT 2501 | Quantitative Foundations for Data Science | Undergraduate |
STAT 8002 | Probability and Statistics Theory II | Graduate |
STAT 8102 | High Dimensional Inference | Graduate |
Selected Publications
Recent
Statistical inference for estimators with distributed data.
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.
Specification tests for covariance structures in high-dimensional statistical models. Virtual.
Joint modeling approaches for longitudinal studies. Philadelphia, PA.
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.
A predictive time-to-event modeling approach with longitudinal measurements and missing data. London, UK.
Specification tests for covariance structures in high-dimensional statistical models. Hangzhou, China.
“A predictive time-to-event modeling approach with longitudinal measurements and missing data. Philadelphia, PA.
High-dimensional statistical inferences with over-identification. Tianjin, China.
Pre-processing with orthogonal decompositions for high-dimensional explanatory variables. Northeastern Normal University, Changchun, China.
A predictive time-to-event modeling approach with longitudinal measurements and missing data. Changchun, China.
(2022). A Predictive Time-to-Event Modeling Approach with Longitudinal Measurements and Missing Data.
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.
Yuan, M., Tang, C., Hong, Y., & Yang, J. (2018). Disentangling and assessing uncertainties in multiperiod corporate default risk predictions. Annals of Applied Statistics, 12(4), 2587-2617. doi: 10.1214/18-AOAS1170.
Chang, J., Guo, J., & Tang, C. (2018). Peter Hall's contribution to empirical likelihood. Statistica Sinica, 28(4), 2375-2387. doi: 10.5705/ss.202017.0059.
Dong, Y., Xia, Q., Tang, C., & Li, Z. (2018). On sufficient dimension reduction with missing responses through estimating equations. Computational Statistics and Data Analysis, 126, 67-77. doi: 10.1016/j.csda.2018.04.006.
Chang, J., Tang, C., & Wu, T. (2018). A new scope of penalized empirical likelihood with high-dimensional estimating equations. Annals of Statistics, 46(6B), 3185-3216. doi: 10.1214/17-AOS1655.
Tang, C. (2017). High-dimensional empirical likelihood and statistical inferences.
Tang, C. (2017). Sufficient dimension reduction with missing responses.
Tang, C. (2017). Pre-processing with orthogonal decompositions for high-dimensional explanatory variables.
Tang, C. (2017). Sufficient dimension reduction with missing responses.