Statistical Methods for Natural Systems
Special Session
17th Biennial Congress on Modelling and Simulation (MODSIM07)
University of Canterbury, Christchurch, New Zealand,
10-13 December 2007
Description
Stochastic models have been used to describe a wide range of natural
phenomena which evolve over time, and are they frequently used as a
first step to parameter estimation, experimental design and hypothesis
testing, and as models for simulation experiments. For example, they
have been used to describe the evolution of populations, the spread of
epidemics, competition between species, the transmission and prevalence
of disease, and the evolution of DNA sequences. Over the past few years
there has been renewed interest in developing statistical procedures for
fitting stochastic models. For systems that be monitored continuously
in time, the theory and practice of statistical inference is well
developed. However, many natural systems can only be sampled at discrete
time points. For example, an animal population might be sampled at the
end of each breeding season, and this explains in part why discrete-time
models are predominant in the ecological and applied population biology
literature. Discrete sampling in the context of continuous-time models
presents many challenging statistical problems: the construction of
estimators, bias reduction, imputation for missing data, and optimal
sampling.
This session, sponsored by the ARC Centre of Excellence for Mathematics
and Statistics of Complex Systems (MASCOS),
brought together researchers and practitioners who
employ statistical and other stochastic methods in genetics, ecology,
epidemiology, population biology and the environment.
Organizers
Dan Pagendam
and
Phil Pollett
<pkp at maths dot uq stop edu period au>
Papers
All papers were refereed by two anonymous reviewers and one of two session editors.
All are available online in the
Electronic
Proceedings.
Selected papers from this session appeared
in Environmental
Modeling & Assessment (Springer).
Submitting author |
Presenter affiliation |
Title of paper |
|
|
|
Shahadat Chowdhury
(presenter)
and Patrick Driver
|
University of New South Wales |
An ecohydrological model
of waterbird nesting events to altered floodplain hydrology
|
Nitin Muttil (presenter)
and K.W. Chau
|
Victoria University Melbourne |
Revealing patterns in coastal
water quality data using statistical analysis
|
Andrew Davey, Patrick Doncaster and Owen Jones (presenter)
|
University of Melbourne |
A stochastic model for shelter use in mobile fish
population: the effect of intraspecific competition
|
Patrick N.J. Lane (presenter),
P.M. Feikema, C.B. Sherwin,
M.C. Peel and A. Freebairn
|
University of Melbourne
|
Physically-based prediction of water
yield from distributed water supply catchments
|
Jay W. Larson (presenter),
E.T. Ong and C. Tokarz
|
Australian National University |
The spheroidal data analysis library and toolkit: tools for
climate model output analysis
|
Julia Piantadosi (presenter),
Boland and Phil Howlett
|
University of South Australia |
Generating synthetic rainfall on various timescales - daily,
monthly and yearly
|
Daniel E. Pagendam (presenter)
and Phil Pollett
|
University of Queensland |
Optimal sampling and problematic likelihood functions in a simple
population model
|
Phil Pollett
|
University of Queensland |
Ensemble behaviour in population processes with applications to ecological
systems
|
Joshua V. Ross (presenter)
and Thomas Taimre
|
King's College Cambridge |
On the analysis of hospital infection data using Markov models
|
L. Augusto Sanabria (presenter)
and R.P. Cechet
|
Geoscience Australia |
Monte-Carlo modelling of severe wind gust
|
Subana Shanmuganathan (presenter),
Philip Sallis
and W. Claster
|
Ritsumeikan Asia Pacific University |
Statistical methods in ecological dynamics modelling
|
George Yu Sofronov (presenter),
G.E. Evans, J.M. Keith and D.P. Kroese
|
University of Queensland |
Identifying change-points in biological sequences via sequential
importance sampling
|
R. Brian Webby (presenter),
David Green and Andrew Metcalfe
|
University of Adelaide |
Modelling water blending - sensitivity of optimal policies
|
|