Current Research Interests

Data-analytic Modeling
Regularization Methods and High Dimensional Modeling
Nonparametric and Robust Methods
Mixed and Mixture Modeling
Statistical Computing

 

 

Selected Publications

• Cheng, Q., Yang, Y. Shi, X. Yeung, K.F., Yang, C. Peng, H. and Liu, J. (2020), MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy, NAR Genomics and Bioinformatics, 2, 149-169.
• Cai, M., Dai, M., Ming, J., Peng, H., Liu, J. and Yang, C. (2020), BIVAS: A scalable Bayesian method for bi-level variable selection with applications, Journal of Computational and Graphical Statistics, 29, 40-52.
• Zhou, M., Dai, M., Yao, Y., Liu, J., Yang, C. and Peng, H. (2019), BOLT-SSI: A Statistical Approach to Screening Interaction Effects for Ultra-High Dimensional Data, arXiv preprint arXiv: 1902.03525.
• Xu, P. Peng, H. and Huang, T. (2018), Unsupervised learning of mixture regression models for longitudinal data, Computational Statistics and Data analysis, 125, 44-56.
• Zhao, J. X., Peng, H. and Huang, T. (2018), Variance estimation for semiparametric regression model by local averaging, Test, 27, 453-476.
• Huang, T., Peng, H. and Zhang, K. (2017), Model Selection for Gaussian Mixture Models, Statistica Sinica, 27, 149-169.
• Li, G. R., Peng, H., Dong, K and Tong, T. J. (2014), Simultaneous Confidence Bands and Hypothesis Testing for Single-index Models, Statistica Sinica, 24, 937-955.
• Cui, X., Peng, H., Wen, S. Q. and Zhu, L. X. (2013), Component selection in an additive models, Scandinavian Journal of Statistics, 40, 491-510.
• Lin, H. Z., and Peng, H., (2013), Smoothed rank correlation of the Linear transformation regression model, Computational Statistics and Data Analysis, 57, 615-630.
• Li, G. R., Peng, H., Zhang J. and Zhu, L. X. (2012), Robust Rank correlation based Screening, The Annals of Statistics, 40, 1846-1877.
• Peng, H. and Lu, Y. (2012), Model Selection in Linear Mixed Effects Models, Journal of Multivariate Analysis, 109, 109-129.
• Peng, H. and Huang, T. (2011), Penalized Least Squares for Single Index Models, Journal of Statistical Planning and Inference, 141, 1362-1379.
• Li, G. R., Peng, H. and Zhu, L. X., (2011), Nonconcave Penalized M-estimation with Diverging Number of Parameters, Statistica Sinica, 21, 391-420.
• Zhang, W. Y. and Peng H., (2010), Simultaneous confidence band and hypothesis test in generalized varying-coefficient models, Journal of Multivariate Analysis , 101, No. 7, 1656-1680.
• Ait-Sahalia, Y., Fan, J. and Peng, H. (2009). Nonparametric transition-based tests for diffusions, Journal of American Statistical Association, Vol 104, No 487, 1102-1116.
• Zhu, L.X., Miao, B.Q., and Peng, H.(2006), On Sliced Inverse Regression with large dimensional covariates, Journal of American Statistical Association, Vol 101, No. 474, 630-643.
• Fan, J., Peng H., and Huang, T., (2005), Semilinear high-dimensional model for normalization of mircoarray data: a theoretical analysis and partial consistency (with discussion), Journal of American Statistical Association, Vol 100, No. 471, 781-796.
• Fan, J. and Peng H., (2004), Nonconcave penalized likelihood with a diverging number of parameters, The annals of statistics, Vol 32, No 3, 928-961.