Fong Shu Chuen Building
Hong Kong Baptist University,
Kowloon Tong, Hong Kong
Regularization Methods and High Dimensional Modeling
Nonparametric and Robust Methods
Mixed and Mixture Modeling
Pei, Y., Peng H. and Xu, J. F. (2022), A Latent Class Cox Model for Heterogeneous Time-to-Event Data, Accepted by Journal of Econometrics.
Fang, K. T., Lin, Y. X. and Peng H. (2022), A new type of robust designs for chemometrics and computer experiments, Chemometrics and Intelligent Laboratory Systems, 221.
Hu, X.,Zhao, J., Lin, Z.., Wang, Y., Peng, H., Zhao, H., Wang, X. and Yang, C. (2022), Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics, Proceeding of the National Academy of Science, 119(28), e2106858119
Zhou, M., Dai, M., Yao, Y., Liu, J., Yang, C. and Peng, H. (2022), BOLT-SSI: A Statistical Approach to Screening Interaction Effects for Ultra-High Dimensional Data, arXiv preprint arXiv: 1902.03525. Accepted by Statistical Sinica
Pei, Y. Huang T., Peng, H. and You, J. (2022), Netowork-Based Clustering for Varying Coefficient Panel Data Models, Journal of Business & Economic Statistics, 40(2), 578-594.
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.
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.
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.
Updated Oct. 7, 2022