Honours in Statistics

If you are an Advanced Mathematics or Advanced Science student, then Honours is built into your program. For all other students, if you are keen on mathematics and statistics and have achieved good results in years 1 to 3, you should consider embarking on an Honours year.

Information on Statistics Honours, including program details, potential research projects and application forms, can be found in the Handbook of Honours in Statistics.

For other information about doing Honours in Statistics, see the Honours page.

Honours Coordinator - Statistics

Dr Libo Li
Email: libo.li@unsw.edu.au  
Phone: 9385 7025
Office: Rm 1035, Red Centre (Centre Wing)

If you have any questions about the Honours year, please don't hesitate to contact Libo.

Statistics Project Areas

The following are suggestions for possible supervisors and honours projects in statistics. A more comprehensive description of the offered projects is available in the Handbook of Honours in Statistics. Other projects are possible, and you should contact any potential supervisors to discuss your options.

Boris Beranger 

  • Analysis of extremes: theory, computations, environmental applications
  • Big and Complex Data analysis

Zdravko Botev

  • Kernel Density Estimation
  • Multilevel and Markov Chain Monte Carlo Methods for Rare-Event Simulation
  • Monte Carlo Methods in Network Reliability

Leung Chan

  • Mathematical finance

Feng Chen

  • Inference and applications of stochastic processes, especially point processes

Diana Combe

  • Combinatorial designs, graph theory and graph labellings

Yanan Fan

  • Bayesian statistics
  • Markov chain Monte Carlo

Gery Geenens

  • Nonparametric and semiparametric density estimation
  • Nonparametric and semiparametric regression, in particular binary regression
  • Functional data analysis

Ben Goldys

  • Stochastic differential equations

Clara Grazian 

  • Mixture models
  • Copula models
  • Bayesian spatial modelling

Pavel Krivitsky

  • Social network analysis
  • Statistical computing

Pierre Lafaye de Micheaux

  • Dependence Measures
  • NeuroImaging
  • Big Data and Internet of Things
  • Statistical Inference for Complex Random Vectors

Libo Li

  • General theory of stochastic processes and its applications in financial mathematics

Jake Olivier

  • Statistical methods for the analysis of count data with applications to epidemiology and population health data

Spiro Penev

  • Wavelet methods in non-parametric inference
  • Latent Variable Models

Donna Salopek

  • Financial modelling
  • Fractional Brownian motion

Scott Sisson

  • Bayesian computational techniques
  • Models of extremes of climate processes
  • Genetic epidemiology

Jakub Stoklosa

  • Analysis of capture-recapture data and estimation of animal abundance
  • Measurement error modelling
  • Non-parametric smoothing

David Warton