Research Grants:

Research interests: methods, theory, and applications in statistics and data science:

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



  • Guo, X. and Tang, C.Y. (2020). Specification tests for covariance structures in high-dimensional statistical models. Biometrika. To appear.
  • Chang, J., Chen, S. X., Tang, C.Y., and Wu, T.T. (2020). High-dimensional empirical likelihood inference. Biometrika. To appear.
  • 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, Theory and Methods. To appear.
  • 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.
  • Tang, C.Y., Fan, Y., and Kong, Y. (2020). Precision matrix estimation by inverse principal orthogonal decomposition. Communications in Mathematical Research. 36 68-92. 


  • Tang, C.Y., Zhang, W., and Leng, C. (2019). Discrete longitudinal data modeling with a mean-correlation regression approach. Statistica Sinica. 29, 853-876.


  • 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.
  • 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.
  • Chang, J.,Guo, J., and Tang, C.Y. (2018). Peter Hall’s contribution to empirical likelihood. Statistica Sinica. 28,2375-2387.
  • Dong, Y., Xia, Q., Tang, C.Y., and Li, Z. (2018). On Sufficient Dimension Reduction with Missing Responses through Estimating Equations. Computational Statistics and Data Analysis. 126,67-77.
  • 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.


  • 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.


  • Wu, T.T., Li,G. and Tang, C.Y. (2015). Empirical likelihood and variable selection for censored linear regression. Scandinavian Journal of Statistics. 42,798-812.
  • 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.


  • 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.
  • Tang, C.Y. and Wu, T.T. (2014). Nested coordinate descent algorithms for empirical likelihood. Journal of Statistical Computation and Simulation. 84,1917-1930.


  • Chang, J., Tang, C.Y. and Wu, Y. (2013). Marginal empirical likelihood and sure independence screening. Annals of Statistics. 41, 2132-2148.
  • Liu, C. and Tang, C.Y. (2013). A state space model approach to integrated covariance matrix estimation with high frequency data. Statistics and Its Interface.6, 463-475.
  • Tang, C.Y. and Fan, Y. (2013). Discussion of “Large covariance estimation by thresholding principal orthogonal complements”. Journal of the Royal Statistical Society, Series B. 75, 671.
  • 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.
  • 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.


  • Tang, C.Y. and Qin, Y. (2012). An efficient empirical likelihood approach for estimating equations with missing data. Biometrika. 99, 1001-1007.
  • Leng, C. and Tang, C.Y. (2012). Sparse matrix graphical models. Journal of the American Statistical Association. 107, 1187-1200.
  • Leng, C. and Tang, C.Y. (2012). Penalized empirical likelihood and growing dimensional general estimating equations. Biometrika. 99, 703-716.
  • Tang, C.Y. and Leng, C. (2012). An empirical likelihood approach to quantile regression with auxiliary information. Statistics and Probability Letters. 82, 29-36.


  • Tang, C.Y. and Leng, C. (2011). Empirical likelihood and quantile regression in longitudinal data analysis. Biometrika. 98, 1001-1006.
  • Chen, S.X. and Tang, C.Y. (2011). Nonparametric regression with discrete covariates and missing values. Statistics and Its Interface. 4, 463-474.
  • Chen, S.X. and Tang, C.Y. (2011). Properties of census dual system population size estimators. International Statistical Review. 79, 336-361.
  • Leng, C. and Tang, C.Y. (2011). Improving variance function estimation in semiparametric longitudinal data analysis. The Canadian Journal of Statistics. 39, 656-670.


  • Tang, C.Y. and Leng, C. (2010). Penalized high dimensional empirical likelihood. Biometrika. 97, 905-920.
  • 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, Applications and Case Studies. 105, 105-119.

2009 and earlier

  • Tang, C.Y. and Chen, S.X. (2009). Parameter estimation and bias correction for diffusion processes. Journal of Econometrics. 149, 65-81.
  • Chen, S.X., Gao, J. and Tang, C.Y. (2008). A test for model specification of diffusion processes. Annals of Statistics. 36, 167-198.
  • Chen, S.X. and Tang, C.Y. (2005). Nonparametric inference of value at risk for dependent financial returns. Journal of Financial Econometrics. 3, 227-255.