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 statistical methods for big and small data analysis.


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


Selected publications:

  1. 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.
  2. Chang, J., Tang, C.Y., and Wu, T.T. (2018). A new scope of penalized empirical likelihood with high-dimensional estimating equations. The Annals of Statistics. 46, 3185-3216.
  3. 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.
  4. Chang, J.,Tang, C.Y. and Wu, Y. (2016).  Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood. The Annals of Statistics.44,515-539.
  5. 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.
  6. 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.
  7. Chang, J., Tang, C.Y. and Wu, Y. (2013). Marginal empirical likelihood and sure independence screening. The Annals of Statistics. 41, 2132-2148.
  8. 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.
  9. 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.
  10. Tang, C.Y. and Qin, Y. (2012). An efficient empirical likelihood approach for estimating equations with missing data Biometrika. 99, 1001-1007.
  11. Leng, C. and Tang, C.Y. (2012). Sparse matrix graphical models. Journal of the American Statistical Association. 107, 1187-1200.
  12. Leng, C. and Tang, C.Y. (2012). Penalized empirical likelihood and growing dimensional general estimating equations. Biometrika. 99, 703-716.
  13. Tang, C.Y. and Leng, C. (2011). Empirical likelihood and quantile regression in longitudinal data analysis. Biometrika. 98, 1001-1006.
  14. Tang, C.Y. and Leng, C. (2010). Penalized high dimensional empirical likelihood. Biometrika. 97, 905-920.
  15. 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.
  16. Tang, C.Y. and Chen, S.X. (2009). Parameter estimation and bias correction for diffusion processes. Journal of Econometrics. 149, 65-81.
  17. Chen, S.X., Gao, J. and Tang, C.Y. (2008). A test for model specification of diffusion processes. The Annals of Statistics. 36, 167-198.
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