School of Mathematics, Statistics & Actuarial Science



Contact Information


Room 357

Office hours: Tu 10:30-11:30/We 10:30-11:30

back to top



Also view these in the Kent Academic Repository

Ali, F. and Zhang, J. (2017). Mixture Model-Based Association Analysis with Case-Control Data in Genome Wide Association Studies. Statistical Applications in Genetics and Molecular Biology.
Zhang, J. (2016). Screening and Clustering of Sparse Regressions with Finite Non-Gaussian Mixtures. Biometrics [Online]. Available at:
Zhang, J. and Oftadeh, E. (2016). Principal Variable Analysis: Multivariate Variable Selection through Use of Null-Beamforming. TBD.
Zhang, J. and Liu, C. (2015). On Linearly Constrained Minimum Variance Beamforming. Journal of Machine Learning Research.
Ali, F. and Zhang, J. (2015). Search for Risk Haplotype Segments with GWAS Data by Use of Finite Mixture Models. Statistics and its interface [Online] 9:267-280. Available at:
Ali, F. and Zhang, J. (2015). Screening tests for Disease Risk Haplotype Segments in Genome by Use of Permutation. Journal of Systems Science and Mathematical Sciences [Online] 35:1402-1417. Available at:
Zhang, J. (2015). On Nonparametric Feature Filters in Electromagnetic Imaging. Journal of Statistical Planning and Inference [Online] 164:39-53. Available at:
Zhang, J., Liu, C. and Green, G. (2014). Source Localization with MEG Data: A Beamforming Approach Based on Covariance Thresholding. Biometrics [Online] 70:121-131. Available at:
Zhang, J. (2013). Epistatic Clustering: A Model-Based Approach for Identifying Links Between Clusters. Journal of the American Statistical Association [Online] 108:1366-1384. Available at:
Zhang, J. (2012). Generalized plaid models. Neurocomputing [Online] 79:95-104. Available at:
Zhang, J. and Liang, F. (2010). Robust Clustering Using Exponential Power Mixtures. Biometrics [Online] 66:1078-1086. Available at:
Zhang, J. (2010). A Bayesian model for biclustering with applications. Journal of the Royal Statistical Society: Series C (Applied Statistics) [Online] 59:635-656. Available at:
Zhang, J. (2009). Learning Bayesian networks for discrete data. Computational Statistics and Data Analysis [Online] 53:865-876. Available at:
Xu, C. et al. (2009). Dense-phase pneumatically conveyed coal particle velocity measurement using electrostatic probes and cross correlation algorithm. Journal of Physics: Conference Series [Online] 147:12004. Available at:
Zhang, J. and Liang, F. (2008). Estimating the false discovery rate using the stochastic approximation algorithm. Biometrika [Online] 95:961-977. Available at:
van Greevenbroek, M. et al. (2008). Effects of interacting networks of cardiovascular risk genes on the risk of type 2 diabetes mellitus (the CODAM study). BMC Medical Genetics [Online] 9. Available at:
Zhang, J. et al. (2008). Inflammatory Gene Haplotype-Interaction Networks Involved in Coronary Collateral Formation. Human Heredity [Online] 66:252-264. Available at:
Zhang, J. and Liang, F. (2008). Convergence of Stochastic approximation algorithm under irregular conditions. Statistica Neerlandica [Online] 62:393-403. Available at:
Ahmad, N. et al. (2006). On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing. Biochimica Et Biophysica Acta-Proteins and Proteomics [Online] 1764:1179-1187. Available at:
Fan, J. and Zhang, J. (2004). Sieve empirical likelihood ratio tests for nonparametric functions . Annals of Statistics 32:1858-1907.
Zhang, J. and Gijbels, I. (2003). Sieve empirical likelihood and extensions of the generalized least squares. Scandinavian Journal of Statistics [Online] 30:1-24. Available at:
Zhang, J. et al. (2003). Search for haplotype interactions that influence susceptibility to type 1 diabetes, through use of unphased genotype data . American Journal of Human Genetics 73:1385-1401.
Fan, J., Zhang, C. and Zhang, J. (2001). Generalized likelihood ratio statistics and Wilks phenomenon. Annals of Statistics 29:153-193.
Total publications in KAR: 23 [See all in KAR]
back to top

Research Interests

  • Non-parametric statistics and high-dimensional statistics
  • Bioinformatics and computational biology
  • Statistical genetics
  • Neuroimaging methods
  • Bayesian modelling.
back to top


MA636/MA836: Stochastic Processes
MA885: Stochastic Processes and Time Series back to top

Research Supervisees

  • Elaheh Oftadeh - latent variable approaches to modelling transcriptomic data in cancer biology and developing statistical methods for heterogeneous population models.


back to top

School of Mathematics, Statistics and Actuarial Science (SMSAS), Sibson Building, Parkwood Road, Canterbury, CT2 7FS

Contact us

Last Updated: 07/07/2017