Fong Shu Chuen Building 

Department of Mathematics,

Hong Kong Baptist University,

Kowloon Tong, Hong Kong


(852)-3411 7021



Heng  Peng


Department of Mathematics,

Hong Kong Baptist University





 B.S.  (1997), The University of Science and Technology of China  (USTC)
 M.S.  (2000), The University of Science and Technology of China
 Ph.D. (2003), The Chinese University of Hong Kong  (CUHK)



 Working Experience

   12/2003-6/2006    Research Associate,  Department of Operation Research and Financial
      Engineering, Princeton University
   9/2006-8/2014    Assistant Professor, Department of Mathematics, HKBU
   9/2014-6/2023    Associate Professor, Department of Mathematics, HKBU
    7/2023- Present
    Professor,  Department of Mathematics, HKBU



  MATH 2206  Probability and Statistics
 MATH 3806  Multivariate Statistical  Methods
 MATH 3805  Regression Analysis
 MATH4826   Time Series and Forecasting
MATH3826   Markov Process and Queuing Theory
GFQR1307   Hands on Little and Big Data



 Research Interesting

       Data-analytic Modeling

       Regularization Methods and High Dimensional Modeling

       Nonparametric and Robust Methods

       Mixed and Mixture Modeling 

      Statistical Computing 




Selected Publications

Scopus No: 26647655700,  

Research ID:  B-7152-2009

Google Scholar

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 statisticsProceeding 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.

  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 ParametersStatistica 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 Aug. 24, 2023