Professor of Statistics and
BiostatisticsDepartment of Statistics George Washington University
801 22nd St. NW
Washington, D.C. 20052 firstname.lastname@example.org Tel: 202-994-7844 Fax: 202-994-6917
· 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
· Aug 2013--------------: 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
· 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
· 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
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)
1. 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.
2. 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.
3. Lian, H., Liang, H. and Carroll, R. (2015). Variance function partially linear single-index models. JRSSB, 77, 171-194.
4. 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.
5. Wu, H., Lu, T., Xue, H. and Liang, H. (2014). Sparse additive ODEs for gene regulatory network modeling. JASA, 109, 700-716.
6. Zhang, X. Y., Zou, G. H. and Liang, H. (2014). Model averaging and weight choice in linear mixed effects models. Biometrika, 101, 205-218.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. Liang, H. and Li, R. Z. (2009). Variable selection for partially linear models with measurement errors. JASA, 104, 234-248.
14. 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.
15. 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.
16. Liang, H., Wu, H.L, and Zou, G. H. (2008). A note on conditional AIC for linear mixed-effects models. Biometrika, 95, 773-778.
17. Liang, H., Thurston, S., Ruppert, D., Apanasovich, T., and Hauser, R. (2008). Additive partial linear models with measurement errors. Biometrika, 95, 667-678.
18. Li, R.Z. and Liang, H. (2008). Variable selection in semiparametric regression modeling. Annals of Statistics, 36, 261-286.
19. Liang, H.,Wang, S.J., and Carroll, R. (2007). Partially linear models with missing response variables and error-prone covariates. Biometrika, 94, 185-198.
20. Liang, H. (2006). A new method of evaluating antitumor activity from measured tumor volumes. Contemporary Clinical Trials, 27, 269-273.
21. 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.
22. Liang, H., Wang, S. J., Robins, J. and Carroll, R. (2004). Estimation in partially linear models with missing covariates. JASA, 99, 357-367.
23. 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.
Hua Liang email@example.com