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 Research Teaching STAT270
STAT310
STAT405
Personal Martin Hazelton's webpage Contact me martin.hazelton AT otago.ac.nz Department of Mathematics and Statistics University of Otago Dunedin New Zealand |
Martin
Hazelton's
Personal Webpage About Me Hello and welcome to my webpage. I am Professor of Statistics in the Department of Mathematics and Statistics at the University of Otago, in beautiful Dunedin, New Zealand Aotearoa. For Current Otago Students For information on papers that I currently teach, see the paper codes under the Teaching heading in the panel on the left. If you are looking for advice on which statistics paper(s) to take, then feel free to contact me. If you are considering doing Honours or a PhD in Statistics and think that you might like to be supervised by me, then read about my research below. About My Research I have a variety of research interests. These include: Smoothing Methods I have long been interested in kernel smoothing problems, and in particular spatially adaptive methods for multivariate data. Other areas of interest include kernel deconvolution problems and constrained spline smoothing. Spatial Statistics A spatial point pattern is a dataset comprising the locations of things or events. A major focus of my research at present is developing better tools for model and inference for spatial patterns. Most recently I have been considering replicated point patterns, where multiple observations are taken on individuals or regions, e.g. locations of cells in repeated biopsies on multiple patients, or locations of animals at various sites through time. This work is in collaboration with Tilman Davies, Adrian Baddeley, Bethany Macdonald, Ege Rubak, and Rasmus Waagerpetersen. Statistical Modelling and Inference in Transportation Science Transportation science generates a huge range of fascinating problems. I'm focused on network tomography (in essence, statistical methods for learning about high dimensional properties of network traffic flows based on lower dimensional observations), and modelling and inference for day-to-day dynamic traffic networks. Statistical Linear Inverse Problems and Z-Polytope Sampling Statistical linear inverse problems are characterized by the linear system y = Ax where y is a vector of observed data and x is the variable of principal interest.  The
configuration matrix A typically has (many) more columns
than rows, so that the linear system is
under-determined. A classic example is network
tomography, where we want to know about traffic flows x
on paths through the network but we observe only traffic
counts y
at various network locations.
Other examples with the same structure include
(re)sampling entries of a contingency table conditional
on various marginal totals, counts of individual animals
in capture-recapture experiments in ecology where
misidentification may occur (so that the true counts x
differ from the observed counts y),
and assessment of items for biosecurity risk under
stratified sampling. When the data are counts, the observations y constrain the variables of interest x to lie in a Z-polytope - that is, the grid of integer valued coordinates (yellow dots in the figure to the right) within a multidimensional polyhedron. Practical methods of statistical inference (like MCMC) require that we sample vectors x lying in this Z-polytope. This is typically done using a random walk. The problem then is to construct a random walk that traverses the Z-polytope efficiently and yet always remains within its bounds. It turns out that this is a hard problem! I'm working on this topic with PhD student Linus Fromm, and studying applications to biosecurity surveillance with Andrew Robinson. Other Research Topics In addition to these medical areas, I have a general interest in the application of statistical methods. Indeed, one of the great things about working in statistics is that I've had the opportunity to look at a diverse range of intriguing problems from a wide variety of areas, from archaeology, to finance, to zoology. Other Stuff Awards I am an honorary life member of was the New Zealand Statistical Association, and was the recipient of the 2014 Littlejohn Research Award, the Association's premier research award. I was the 2025 E.A. Cornish Lecturer. Editorial I was Editor-in-Chief of the Australian and New Zealand Journal of Statistics from 2019-2025. |
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last updated: 11 February 2026.