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
If you have any questions about the Honours year, please don't hesitate to contact Gery.
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.
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- Analysis of extremes: theory, computations, environmental applications
- Big and Complex Data analysis
- Kernel Density Estimation
- Multilevel and Markov Chain Monte Carlo Methods for Rare-Event Simulation
- Monte Carlo Methods in Network Reliability
- Mathematical finance
- Inference and applications of stochastic processes, especially point processes
- Combinatorial designs, graph theory and graph labellings
- Bayesian statistics
- Markov chain Monte Carlo
- Statistical dependence: modelling, quantification and analysis
- Copula modelling: theory and applications
- Nonparametric and semiparametric estimators for probability densities and regression functions
- Sports analytics (mainly football/soccer)
- Stochastic differential equations
- Mixture models
- Copula models
- Bayesian spatial modelling
- Social network analysis
- Statistical computing
- Dependence Measures
- NeuroImaging
- Big Data and Internet of Things
- Statistical Inference for Complex Random Vectors
- General theory of stochastic processes and its applications in financial mathematics
- Statistical methods for the analysis of count data with applications to epidemiology and population health data
- Wavelet methods in non-parametric inference
- Latent Variable Models
- Financial modelling
- Fractional Brownian motion
- Bayesian computational techniques
- Models of extremes of climate processes
- Genetic epidemiology
Eva Stadler (The Kirby Institute)
- Statistical analysis of data relating to infectious diseases (malaria or COVID-19)
- Analysis of capture-recapture data and estimation of animal abundance
- Measurement error modelling
- Non-parametric smoothing
- Ecological statistics: see UNSW Eco-Stats Opportunities
- High-dimensional data analysis
- Species distribution modelling
- Analysis of extremes: theory, computations, environmental applications