Department of Statistics
Research areas
Our staff are engaged in ground breaking research which is recognised internationally. Read more about our areas of research below.
Statisticians at Auckland are developing new statistical methods for a whole range of problems, many of which originally came to the notice of the statistical community in a medical or biostatistical context, but whose solutions are generally applicable. Much of this work relates to forms of regression, i.e. to finding novel ways to understand or predict the behaviour of variables of compelling interest using information gathered on other variables.
Examples include models and generalised estimating equation methods to better understand correlated binary data, improved methods for longitudinal data analysis, using smoothing techniques to reveal relationships in multivariate data without making restrictive assumptions, adapting regression methods to allow novel approaches to sampling that can increase the efficiency of observational studies (e.g., generalisations of case-control sampling).
The people with the greatest involvement in these types of research are Alastair Scott, Alan Lee, Chris Wild, Yong Wang, Thomas Yee and Thomas Lumley. Several researchers are also actively involved in practical medical research, most notably Patricia Metcalf and Thomas Lumley.
This Department is the birthplace of R (R is the language and environment for statistical computing and graphics and home to a growing number of statisticians who do substantial work on statistical computing and computational statistics.
Find more information about the 'R' project
Standard commercial statistical packages are good for routine analyses of sets of data without non-standard features. Such data sets are rarer than you might think. Commercial packages are fast and implement algorithms and procedures that were seen to be viable at the time the package was written.
For statistical research one requires more flexibility than these packages generally provide. A statistician has an idea for a non-standard approach to the problem and wants to try it out. In these situations a more flexible system is required. It is not the time it takes the computer to execute the commands that is important but rather the time it takes the statistician to develop new software. Ongoing research and software development in the Statistics Department is building computing environments that increase the effectiveness and efficiency of the statistician. There is associated research on statistical graphics, on developing software to implement specific new statistical methods and on computer-intensive statistical methods such as Markov Chain Monte Carlo.
The people with the greatest involvement in these types of research are Ross Ihaka (joint founder of R along with then Department member Robert Gentleman), Paul Murrell (R Core Group developer with a primary interest in graphics), Thomas Lumley, Thomas Yee, Yong Wang, and David Scott.
Over the years, researchers have been analysing data from complex surveys as if it came from random samples. The results of such analyses can be extremely misleading. Standard methods have to be adapted to allow for the special features of survey data.
Alastair Scott has been a world authority in this area for many years. Research is also being conducted into novel ways of obtaining and analysing market research data, multilevel modelling, the effects of interviewer variability on subsequent analyses, and the design of panel studies.
Senior Tutor Andrew Balemi talks about Market Research and why we buy what we buy.
Our department has an extensive and world-renowned research group in statistical ecology and bioinformatics, whose members have a broad range of areas of expertise and interests.
Building on the great legacy of George Seber, a senior professor and world authority on the estimation of animal abundances, we have Rachel Fewster, who works on statistical methods for population abundance assessment - how many whales are there in the ocean, or goats in the Hunua Ranges? She also works with models to investigate how a population spreads through its environment, for example rats colonizing islands off the NZ coast or rare birds surviving in a managed forest habitat.
Brian McArdle works on multivariate modeling of ecological communities, including the development of novel statistical methods for environmental sampling and assessment in marine and terrestrial environments.
Russell Millar specialises in the modelling of marine fish populations from catch data. In particular, he uses recently developed likelihood and Bayesian techniques for analysing things like gear selectivity, fish growth and doing sequential population analysis.
Nick Shears combines marine ecological research and a variety of statistical techniques to address marine conservation and management questions, such as investigating how ecosystems respond to marine reserve protection and how sedimentation affects rocky reef assemblages.
Chris Triggs specialises in experimental design and generalised linear models, particularly in their application to microarray experiments, population genetics, and crop variety trials.
Stéphane Guindon is developing probabilistic models of molecular evolution. These models are used to detect traces of natural selection amongst genetic sequences and estimate dates of divergence between ancestral species.
Thomas Yee has made important contributions to statistical modelling in plant ecology and retains an active interest in the area.
The most recent members of our team are James Russell who applies statistical methods to problems in island conservation and ecology and Thomas Lumley who works on statistical problems arising from genomic studies of heart disease.
