Stats Society NSW Monthly Meeting: Anderson -- Identifying Boundaries in Spatial Modelling

Tomorrow night is the May meeting for the NSW Branch of the Statistical Society. 
This month we are in the funky new UTS building designed by Frank Gehry!
Details are below. All are welcome.

Cheers,

Michael

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Date:  Tuesday, 26 May 2015

Time:
6:00pm - 6:30pm: Refreshments
6:30pm - 7:30pm: Lecture
7:45pm - onwards: Dinner (at a nearby restaurant)

Venue:
Room 002, Level 5, University of Technology, Sydney - Building 8 (Dr Chau
Chak Wing Building), 14 Ultimo Rd, Haymarket NSW 2000

Dr. Craig Anderson
University of Technology, Sydney

Identifying Boundaries in Spatial Modelling

Disease mapping is the field of spatial epidemiology interested in
characterising how disease risk across different geographical regions. A
key aim is to identify regions which exhibit significantly elevated disease
risk levels, thus allowing public health interventions to be focused on
these areas.  Bayesian models utilising a so-called Conditional
Auto-Regressive (CAR) structure are typically used in these settings.
These models usually assume a spatially smooth risk surface across the
entire region, but this is not necessarily realistic in practice.  Using a
case study of respiratory hospital admissions in Glasgow, Scotland, a city
with many localised inequalities, I will present two alternative approaches
which use clustering techniques to allow for discontinuities in the spatial
structure.   One of these approaches utilised Integrated Nested Laplace
Approximation (INLA), and I will touch on its use as a computationally
efficient tool for approximate Bayesian inference.

Biography of Dr. Craig Anderson
Craig Anderson graduated with an Honours degree in Statistics from the
University of Glasgow, and then achieved his PhD in Statistics within the
same department.  After completing his PhD, he moved to Australia to take
up a position as a Postdoctoral Research Fellow at the University of
Technology, Sydney.  He is interested in statistics for health data, with a
particular focus on spatial and spatio-temporal modelling of disease risk.