Dr Lyn Hunt
Senior Lecturer (Software Engineering)
Qualifications: MSc DPhil Waikato
Phone: +64 7 838 4466
Lyn initially joined the Department in 1985 as a graduate student before taking on the role of Tutor in 1988 and Senior Tutor in 1995. Lyn completed a doctorate degree and attained a lecturing position in the Department of Statistics in 1996. Previously Lyn had worked in actuarial positions in the Government Life Office and in an insurance company and also as a chemist monitoring air pollution in New Zealand.
Lyn teaches the first year papers 'Statistics for Science' (STAT111) as well as 'Introduction to Statistical Methods' (STAT121). These courses are mainly taken by students from the schools of Science, and Computing and Mathematical Sciences. The courses have an emphasis on statistical thinking, i.e. interpretation of the computer output for the questions of interest.
Lyn also teaches Statistics for Quality Improvement (STAT352A) and is part of the teaching team for 'Design and Analysis of Experiments and Surveys' (STAT323).
An activity that I have taken up recently with my family is kayaking. So far this has been mainly done in the sea at the Bay of Islands, but we are now going out at Lake Karapiro.
I also enjoying sea fishing and 'pottering away' in our garden in my spare time.
Lyn's research over the past years concerns the clustering of observations using a finite mixture models approach, and where the EM algorithm is employed in the fitting of the model.The model can accommodate both categorical and continuous variables. (Jorgensen and Hunt (1996), Hunt and Jorgensen 1999). Lyn has written a FORTRAN program, Multimix, to estimate the parameters of the distributions when fitting a mixture of K groups, by using this approach.
The approach has been extended so that missing observations can also be accommodated in the model. The program implementing this approach will be released shortly.
Lyn has worked with Associate Professor Kaye Basford, University of Queensland on extending this clustering approach to three-way data sets for mixed datasets (Hunt & Basford 1999). As missing data values are also a problem in three-way data sets, the approach has been extended for data sets with continuous attributes (Hunt & Basford 2001). Our latest work involves extending the technique used for clustering three way data so that these techniques can cope with missing values for datasets with mixed attributes.
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