Hua Liang

Hua Liang
Title:
Professor of Statistics
Office:
754
Address:
Rome Hall
801 22nd St NW
Washington, DC
Phone:
202-994-7844
Email:
[email protected]

Areas of Expertise

  • Partially Linear Models
  • High-Dimensional Semi-parametric Modeling
  • Model Averaging and Model Selection
  • Longitudinal Data Analysis
  • Measurement Error Models
  • Nonlinear and Nonparametric Mixed Effect Models
  • HIV/AIDS Clinical Trial and Dynamic Modeling

Experience

  • Aug 2013---present:  Professor, Department of Statistics, The George Washington University, Washington, D.C. 20052
  • Feb 2009---Aug 2013: Professor, Department of Biostatistics and Computational Biology, Uni­versity of Rochester Medical Center, Rochester, NY 14642
  • Aug 2005---Jan 2009: Associate Professor, Department of Biostatistics and Computational Biol­ogy, University of Rochester Medical Center, Rochester, NY 14642
  • Jun 2000---May 2002: Assistant Member, Department of Biostatistics, St.  Jude Children's Re­search Hospital, Memphis, TN 38105
  • Jun 2000---May 2002: Research Associate, Frontier Science Foundation, Chestnut Hill, MA 02467
  • May 1996---Feb 1998: Alexander von Humboldt Research Fellow, Humboldt University, Berlin, Germany (Host Professor: Dr. Wolfgang Haerdle)
  • Dec 1992---Dec 1998: Assistant Professor, Associate Professor, Institute of Systems Science, Chi­nese Academy of Sciences

Education

  • Ph.D. degree in statistics in 2001 under the direction of Professor Raymond J. Carroll, Texas A&M University
  • Ph.D. degree in mathematical statistics in 1992 under the direction of Professor Ping Cheng, Chinese Academy of Sciences

Publications

Books

  • Härdle, W., Liang, H., and Gao, J. T. (2000). Partially Linear Models. Springer Phisica-Verlag, Germany.
  • Liang, H. (2008). Related Topics in Partially Linear Models, VDM Verlag, Saarbrucken, Germany.

Selected Papers

  • Zhou, L., Lin, H. Z. and Liang, H. (2018). Efficient estimation of the nonparametric mean and covariance functions for longitudinal and sparse functional data. JASA, 113, 1550-1564.
  • Zhang, X. Y., Yu, D. L., Zou, G.H. and Liang, H. (2016). Optimal model averaging estimation for generalized linear models and generalized linear mixed-effects models. JASA, 111, 1775-1790.
  • Ma, S. J., Carroll, R., Liang, H. and Xu, S. Z. (2015). Generalized additive coefficient models for gene-environment interactions.  Annals of Statistics, 43, 2102-2131.
  • Chen, J., Li, D., Liang, H. and Wang, S.J. (2015). Semiparametric GEE analysis in partially linear single-index models for longitudinal data. Annals of Statistics, 43, 1682-1715.
  • Lian, H., Liang, H. and Carroll, R. (2015). Variance function partially linear single-index models. JRSSB,  77, 171-194.
  • Wang, L., Xue, L., Qu, A. and Liang, H. (2014). Estimation and model selection in generalized additive partial linear models for high-dimensional correlated data. Annals of Statistics, 42, 592-624.
  • Wu, H., Lu, T., Xue, H. and Liang, H. (2014). Sparse additive ODEs for gene regulatory network modeling. JASA, 109, 700-716.
  • Zhang, X. Y., Zou, G. H. and Liang, H. (2014). Model averaging and weight choice in linear mixed effects models. Biometrika, 101, 205-218.
  • Lu, T. Liang, H., Li, H.Z., and Wu, H. L. (2011). High dimensional ODEs coupled with mixed-effects modeling techniques for dynamic gene regulatory network identification. JASA, 106, 1242-1258.
  • Liang, H., Zou, G.H., Wan, A. T. K., and Zhang, X. Y. (2011). Optimal weight choice for frequentist model average estimators. JASA, 106, 1053-1066.
  • Wang, L., Liu, X., Liang, H. and Carroll, R. (2011). Estimation and variable selection for generalized additive partial linear models. Annals of Statistics, 39, 1827-1851.
  • Zhang, X. Y. and Liang, H. (2011). Focused information criterion and model averaging for generalized additive partial linear models. Annals of Statistics, 39, 174-200.
  • Liang, H., Liu, X., Li, R. and Tsai, C.L. (2010). Estimation and testing for partially linear single index models. Annals of Statistics, 38, 3811-3836.
  • Du, P., Ma, S. and Liang, H. (2010). Penalized variable selection procedure for Cox models with semiparametric relative risk. Annals of Statistics, 38, 2092-2117.
  • Liang, H. and Li, R. Z. (2009). Variable selection for partially linear models with measurement errors. JASA, 104, 234-248.
  • Zhou, Y. and Liang, H. (2009). Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates. Annals of Statistics, 37, 427-458.
  • Liang, H. and Wu, H. L. (2008). Parameter estimation for differential equation models using a framework of measurement error in regression models. JASA, 103, 1570-1583.
  • Liang, H., Wu, H.L, and Zou, G. H. (2008). A note on conditional AIC for linear mixed-effects models. Biometrika, 95, 773-778.
  • Liang, H., Thurston, S., Ruppert, D., Apanasovich, T., and Hauser, R. (2008). Additive partial linear models with measurement errors. Biometrika, 95, 667-678.
  • Li, R.Z. and Liang, H. (2008). Variable selection in semiparametric regression modeling. Annals of Statistics, 36, 261-286.
  • Liang, H.,Wang, S.J., and Carroll, R. (2007). Partially linear models with missing response variables and error-prone covariates. Biometrika, 94, 185-198.
  • Liang, H. (2006). A new method of evaluating antitumor activity from measured tumor volumes. Contemporary Clinical Trials, 27, 269-273.
  • Zhou, Y. and Liang, H. (2005). Empirical-likelihood-based semiparametric inference for the treatment effect in the two-sample problem with censoring. Biometrika, 92, 271-282.
  • Liang, H., Wang, S. J., Robins, J. and Carroll, R. (2004). Estimation in partially linear models with missing covariates. JASA, 99, 357-367.
  • Liang, H., Härdle, W. and Carroll, R. J. (1999). Estimation in a semiparametric partially linear errors-in-variables model. Annals of Statistics, 27. 1519-1535. 

Professional Service

Associate Editor: JASA (2008-2011, 2014-); Journal of Nonparametric Statistics (2009-2013); Jour­nal of Systems Science and Complexity (2009-2013); Biostatistics (2010-2013); Electronic Journal of Statistics (2011-2014)

Distinctions

  • Fellow of ASA, IMS, the Royal Statistical Society
  • Elected member of the International Statistical Institute
  • H. O. Hartley Award, Texas A&M University, Department of Statistics