Massey University






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m.hazelton AT massey.ac.nz

tel: +64 6 356 9099 x84642
fax: +64 6 355 7953


Institute of Fundamental Sciences
Massey University
Private Bag 11222
Palmerston North 4442
New Zealand


Martin Hazelton's Personal Webpage

About Me
Hello and welcome to my webpage. I am Professor of Statistics and Acting Head of the Institute of Fundamental Sciences at Massey University's Manawatu campus in Palmerston North, New Zealand.

For Current Massey Students
For information on papers that I currently teach, see the Massey paper codes under the Teaching heading in the panel on the left. Access to information on those papers is available throught the Stream learning environment.

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. My current work in this area includes kernel estimation of relative risk functions in geographical epidemiology, with former PhD student Tilman Davies (the latter now a senior lecturer at the University of Otago). Other areas of intetrest include kernel deconvolution problems and constrained spline smoothing.

Biostatistics and Applied Statistics

I have a keen interest in the development and application of statistical methods in medicine, particularly epidemiology and opthalmology. I am currently working with Professor Bill Morgan (Lions Eye Institute, Western Australia) on some challenging statistical modelling problems for ophthalmic data collected from glaucoma patients. I am also a Principal Investigator in Massey's Infectious Disease Research Centre (IDReC), where my work includes modelling patterns of foot and mouth disease in Vietnam with epidemiologist Prof Mark Stevenson and former PhD student Kate Richards, and estimation of spatial risk (as discussed under Smoothing Methods above).

Spatial Statistics
Through my interests in smoothing, networks, and geographical epidemiology, I have an evolving interest in spatial statistics. Much of Tilman Davies' later work was in this area.

I am Associate Investigator on a New Zealand Royal Society Fast Start Marsden Fund grant entitled "Smoothing and inference for point process data with applications to epidemiology" for 2016-2019. The Principal Investigator is Tilman Davies.

Statistical Modelling and Inference in Transportation Science
Transportation science generates a huge range of fasinating problems. I'm currently focused on network tomography (in essence, statistical methods for learning about high dimensional properies of network traffic flows based on lower dimensional observations), and modelling and inference for day-to-day dynamic traffic networks, with Professors David Watling, Giulio Cantarella, Hong Lo and Mike Smith. I was awarded a New Zealand Royal Society Marsden Fund grant for 2015-2019 to work in this area. Iranian PhD student Ahmad Mahmoodjanlou is working with me on this project. He is co-supervised by Dr Katharina Parry, who recently joined Massey as a Lecturer in Statistics.

Statistics Linear Inverse Problems and Polytope Sampling
Network tomography is an example of a statistical linear inverse problem. These are characterized by the linear system y = Ax where y is a vector of observed data (e.g. traffic counts on road segments), x is the variable of principal interest (e.g. traffic volumes between different zones of a network). PolytopeThe configuration matrix A typically has (many) more columns than rows, so that the linear system is under-determined. Other examples with the same structure include (re)sampling entries of a contingency table conditional on various marginal totals, and 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).

When the data are counts, the observations y constrain the variables of interest x to lie in a lattice 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 polytope. This is typically done using a random walk. The problem then is to construct a random walk that traverses the polytope efficiently and yet always remains within its bounds. It turns out that this is a hard problem!

In collaboration with Professor Alan Lee (University of Auckland), Dr Matt Schofield (University of Otago) and Dr Rina Parry (Massey University), I was recently awarded a new Marsden grant, 17-MAU-037 Lattice polytope samplers: theory, methods and applications (2018-2020), by the Royal Society of New Zealand to work on this topic. PhD student Mike McVeagh has joined the team, and I am current advertising for a Postdoctoral Fellow in Statistics  to work on computational aspects of the project.

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.

 I was the recipient of the 2014 Littlejohn Research Award, the New Zealand Statistical Association's premier research award.







Page last updated: 4 April 2018.