JOINT ST ANDREWS/HIGHLANDS RSS GROUP MEETING
Wednesday 2 May
Lecture Theatre D,
Mathematical Institute,
North Haugh, St Andrews
(for updates please go to
http://www.mcs.st-and.ac.uk/StatsSeminars/index.shtml,
a map of the university can be found at
http://www.st-andrews.ac.uk/map/map.pdf)
3:00: Professor Chris Jones (Open University)
"Don't Mention Stonehenge! The Statistician's Side of the Story of a
Ten Year Collaboration between Archaeology, Earth Sciences and Statistics"
Abstract:
The subtitle is the short version of the abstract! In 1994,
archaeologist Olwen Williams-Thorpe helped me out by appearing on an
Open University television programme associated with a Statistics
course. In return, I agreed to help her out with a bit of statistical
consultancy. More than 10 years - and 10 papers - later, this
collaboration has come to an end. In this talk, I'll review this
successful collaboration between archaeology, earth sciences and
statistics. Our work focussed on the magnetic and chemical properties of
stone artefacts and what these quantitative measurements could tell us
about their provenance: from Roman columns quarried in the Egyptian
desert to British Bronze Age stone axes sourced from the Whin Sill in
Northern England and the Preseli Mountains in South Wales. The talk will
be broad-brush, yet at times selective with an emphasis on the
statistics, anecdotal, and perhaps even occasionally amusing, rather
than going into the scientific outcomes in depth (particularly as I am
no archaeologist or geologist).
4:00: Tea (in the Staff Room)
4:30: Professor John Hinde (National University of Ireland,Galway)
"Random Effects, Mixtures and NPMLE"
Abstract:
The use of mixed models with normally distributed response and random
effects is well worked out and available in many software packages. For
non-normal response data the situation is less clear and there are many
different approaches being studied. Within the framework of generalized
linear models, one natural way of proceeding is to include additive
random effects in the linear predictor. These random effects can be used
to account for an additional level of individual variability
(overdispersion); a shared random effect for the additional variance
component in two-stage sample designs; longitudinal dependence in
repeated measures designs; spatial dependence in disease mapping; etc.
A common assumption for the random effects is that they are normally
distributed, giving the generalized linear mixed model, where maximum
likelihood estimates can be obtained using the EM algorithm and
essentially fitting a mixture model. By a simple extension of this
approach, it is possible to relax the assumption of normality and obtain
a nonparametric maximum likelihood estimate for the random effects
distribution.
In this talk we will give a brief introduction to the methodology and
discuss several illustrative examples using the recent implemented R
package npmlreg.
The meeting will be followed at 6.30 p.m. by a 2-course meal at the
Doll's House Restaurant in Church Square, St Andrews. The menu is
available at http://www.dolls-house.co.uk. (Click on "menus" and go
to "early evening menu".)
If you intend to come to the meal, please inform Peter Jupp,
preferably by email ([log in to unmask]) before 5:00 p.m. on Monday 30
April.
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Dr. P. E. Jupp
School of Mathematics and Statistics
University of St. Andrews
North Haugh, St. Andrews tel: (44) 1334 463704
Fife, KY16 9SS fax: (44) 1334 463748
Scotland e-mail: [log in to unmask]
url: http://www.mcs.st-andrews.ac.uk/~pej/
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