School of Mathematics, Statistics & Actuarial Science



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Room 357

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

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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 [Online] 16. Available at:
Zhang, J. and Oftadeh, E. (2016). Principal Variable Analysis: Multivariate Variable Selection through Use of Null-Beamforming. TBD.
Zhang, J. (2016). Screening and Clustering of Sparse Regressions with Finite Non-Gaussian Mixtures. Biometrics [Online]. 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:
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:
Zhang, J. and Liu, C. (2015). On Linearly Constrained Minimum Variance Beamforming. Journal of Machine Learning Research.
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. (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. and Liang, F. (2010). Robust Clustering Using Exponential Power Mixtures. Biometrics [Online] 66:1078-1086. 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. (2009). Learning Bayesian networks for discrete data. Computational Statistics and Data Analysis [Online] 53:865-876. 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. and Liang, F. (2008). Estimating the false discovery rate using the stochastic approximation algorithm. Biometrika [Online] 95:961-977. 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. 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.
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:
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]
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Research Interests

  • Non-parametric statistics and high-dimensional statistics
  • Bioinformatics and computational biology
  • Statistical genetics
  • Neuroimaging methods
  • Bayesian modelling.
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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.
  • Jie Li


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

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Last Updated: 20/09/2017