I am Associate Professor and the Seymour Wolfbein Senior Research Fellow in the Department of Statistical Science, Fox School of Business, Temple University.

I earned my Ph.D. in Statistics from the Department of Statistics, Iowa State University. This is my Mathematical Genealogy. My research interests are methods, theory, and applications in statistics and data science.


  • Room 376 Building 1810
    1810 Liacouras Walk
    Philadelphia, PA 19122-6083
  • Phone: (215) 204-3191
  • Email: yongtang at temple.edu


Selected publications:

  1. Chang, J., Chen, S. X., Tang, C.Y., and Wu, T.T. (2020). High-dimensional empirical likelihood inference. Biometrika. To appear.
  2. Bruce, S.A., Tang, C.Y., Hall, M. H., and Krafty, R.T. (2020). Empirical frequency band analysis of nonstationary time series. Journal of the American Statistical Association. To appear.
  3. Tang, C.Y., Fang, E.X., and Dong, Y. (2020). High-dimensional interactions detection with sparse principal hessian matrix. Journal of Machine Learning Research. 21(19) 1-25.
  4. Yuan, M., Tang, C.Y., Hong, Y., and Yang, J. (2018). Disentangling and assessing uncertainties in multiperiod corporate default risk predictions. Annals of Applied Statistics. 12, 2587-2617.
  5. Chang, J., Tang, C.Y., and Wu, T.T. (2018). A new scope of penalized empirical likelihood with high-dimensional estimating equations. Annals of Statistics. 46, 3185-3216.
  6. Chang, J., Delaigle, A., Hall, P., and Tang, C.Y. (2018). A frequency domain analysis of the error distribution from noisy high-frequency data. Biometrika. 105,353-369.
  7. Chang, J.,Tang, C.Y. and Wu, Y. (2016).  Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood. Annals of Statistics.44,515-539.
  8. Zhang, W., Leng, C. and Tang, C.Y. (2015). A joint modeling approach for longitudinal studies. Journal of the Royal Statistical Society, Series B. 77,219-238.
  9. Liu, C. and Tang, C.Y. (2014). A quasi-maximum likelihood approach for integrated covariance matrix estimation with high frequency data. Journal of Econometrics. 180,217-232.
  10. Chang, J., Tang, C.Y. and Wu, Y. (2013). Marginal empirical likelihood and sure independence screening. Annals of Statistics. 41, 2132-2148.
  11. Fan, Y. and Tang, C.Y. (2013). Tuning parameter selection in high dimensional penalized likelihood. Journal of the Royal Statistical Society, Series B. 75, 531-552.
  12. Chen, S.X., Qin, J. and Tang, C.Y. (2013). Mann-Whitney test with adjustments to pre-treatment variables for missing values and observational study. Journal of the Royal Statistical Society, Series B. 75, 81-102.
  13. Tang, C.Y. and Qin, Y. (2012). An efficient empirical likelihood approach for estimating equations with missing data Biometrika. 99, 1001-1007.
  14. Leng, C. and Tang, C.Y. (2012). Sparse matrix graphical models. Journal of the American Statistical Association. 107, 1187-1200.
  15. Leng, C. and Tang, C.Y. (2012). Penalized empirical likelihood and growing dimensional general estimating equations. Biometrika. 99, 703-716.
  16. Tang, C.Y. and Leng, C. (2011). Empirical likelihood and quantile regression in longitudinal data analysis. Biometrika. 98, 1001-1006.
  17. Tang, C.Y. and Leng, C. (2010). Penalized high dimensional empirical likelihood. Biometrika. 97, 905-920.
  18. Chen, S.X., Tang, C.Y. and Mule, V. T. (2010). Local post-stratification  in dual system accuracy and coverage evaluation for the US Census. Journal of the American Statistical Association. 105, 105-119.
  19. Tang, C.Y. and Chen, S.X. (2009). Parameter estimation and bias correction for diffusion processes. Journal of Econometrics. 149, 65-81.
  20. Chen, S.X., Gao, J. and Tang, C.Y. (2008). A test for model specification of diffusion processes. Annals of Statistics. 36, 167-198.
site stats