The statistics research group has active research areas that range from theoretical to applied statistics with a recent shift towards research that has ready application to biomedical data.

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Statistics research areas

Our statistics research group has research strength in the following area:

Computational statistics and statistical learning

Research in statistical learning theory is concerned with finding a predictive function based on data drawing upon different fields of statistics, functional analysis, optimization and computer science. Computational statistics aims to design algorithms for implementing statistical methods on computers, including the ones unthinkable before the computer age (e.g., bootstrap, wavelets, multiscale image processing), as well as the ability to cope with analytically intractable problems. It includes many computationally-intensive statistical methods.

Specific research areas: Model selection and model building, variation Bayes, inverse problems, statistical networks, Markov chain Monte Carlo methods, expectation-maximization methods, density estimation and generalized additive models.

Researchers: L. Azizi, J. Chan, U. Keich, S. Müller, J. Ormerod, M. Stewart, E. Tanaka, J. Yang, P. Yang

Bioinformatics and complex data model

Our research group utilises quantitative reasoning including mathematical modelling, statistical analysis to study complex data and in parallel building algorithmic tools for the analysis of complex systems and high dimensional data. In particular, the areas of bioinformatics examine such datasets that generated through high throughput modern biotechnological assays such as next generation sequencing technologies. Analysing of these data enable us to gain insight into fundamental biological processes.

Specific research areas: Cancer informatics, analysis of omics data, vertical integration, resting-state fMRI data, study of gene regulation and DNA relocation initiation.

Researchers: L. Azizi, U. Keich, S. Müller, J. Ormerod, E. Tanaka, J. Yang P. Yang

Applied probability and stochastic process

This research area focuses on dynamic models of random processes and phenomena. Our research group utilises these method to solve applied problems in various fields such as finance, insurance, biology and medical science. In particular: stochastic volatility models for financial applications, biological modelling, finite and infinite non-negative matrices and their ergodicity and fractional processes.

Specific research areas: Trend diagnostics, model estimation, characterisations of probability distributions, mixture models, lose reserve models, geometric processes, Markov chains and Markov decision processes.

Researchers: L. Azizi, J. Chan, S. Peiris, R. Kawai, M. Stewart, Q Wang

Econometrics and time series

Econometrics refers to the application of mathematics, statistical methods, and computer science, to economic data and aims to give empirical content to economic relations. Time series analysis comprises methods for analysing time series data in order to extract meaningful statistics and other characteristics of the data.

Specific research areas: Nonlinear co-integrating regression, non-stationary time series econometrics, econometric theory, stochastic volatility models, long memory models, value-at-risk and expected shortfall.

Researchers: J. Chan, R. Kawai, S. Peiris, Q. Wang

Statistical theory

Statistical theory provides a fundamental basis of all area in statistics. Asymptotic methods are used in all areas of statistics to provide approximations and are the basis of much of classical probability. In particular, the statistical analysis of extreme values is important for many disciplines, including finance, insurance and environmental sciences. Multivariate extreme value theory investigates among others the analysis of spatial extremes, the estimation of support curves and risk assessment of financial assets

Specific research areas: Extreme value theory, limit theorems for martingales, self-large deviations, saddle-point approximations, nonparametric estimation, change-point analysis and mixture models.

Researchers: R. Kawai, J. Ormerod, J. Robinson, S. Müller, M. Stewart, Q. Wang, N. Weber.

Specialities of individual researchers

Academic and research staff

  • Dr Lamiae Azizi
    Graphical modelling and Variational Techniques, Bayesian non-parametrics, Bayesian modelling, clustering and classification; and spatial and spatio-temporal modeling. Applications of interest include image analysis, complex diseases (e.g. Cancers), FMRI and Genomics data.
  • Associate Professor Jennifer Chan
    Generalised Linear Mixed Models, Bayesian Robustness, Heavy Tail Distributions, Scale Mixture Distributions, Geometric Process for Time Series Data, Applications for Insurance Data.
  • Dr. Ray Kawai
    Statistics and numerical methods for stochastic differential equations, Mathematical ecology, mathematical finance, optimization.
  • Associate Professor Uri Keich
    Bioinformatics: creating tools for the discovery and analysis of sequence motifs, study of DNA replication origins. Computational statistics: designing fast and numerically stable algorithms for evaluating the significance of exact tests.
  • Associate Professor Samuel Müller
    Extreme Value Theory, Model Selection, Robust Methods, Applied Statistics.
  • Dr. John Ormerod
    Variational Approximations, Generalised Linear Mixed Models, Splines, Data Mining, Semiparametric Regression and Missing Data.
  • Associate Professor Shelton Peiris
    Time Series Analysis, Estimating Functions and Applications, Statistics in Finance, Financial Econometrics, Time Dependent Categorical Data.
  • Dr. Michael Stewart
    Mixture Models, Extremes of Stochastic Processes, Empirical Process Approximations, Density Estimation, Feature Selection, Applied and Computational Statistics
  • Dr. Emi Tanaka
    Applied Statistics in Agriculture and Bioinformatics, Linear Mixed Models, Experimental Design, Computational Statistics
  • Associate Professor Qiying Wang
    Nonstationary time series econometrics, Nonparametric statistics, Econometric Theory, Local Time Theory, Self-normalized limit theory.
  • Dr Diana Warren
    Development of Statistical Literacy, Probability Distributions, History of Mathematics and Statistics
  • Professor Jean Yang
    Applied Statistics, Statistical Bioinformatics, Statistical machine learning, Integrative Analysis of Omics Data, Statistical networks and fMRI data.
  • Dr Pengyi Yang
    Signalling Network Reconstruction, Transcription Network Reconstruction, Statistical Learning in Omics, Omic Data Visualisation, Decipher Embryogenesis

Retired members active in research and Visiting Professors