Use of Pareto Frontiers to detect
deficiencies in a biological simulation model. Proceedings
of the IEEE Congress on Evolutionary Computation, CEC
2007,Singapore, Sept. 25-28, 2007. Defining the
model assessment problem as a problem in evolutionary
computation optimization. Further illustration of how to
interrogate the model's Pareto frontier to reveal the sources
of structural deficiencies in a model of branch shoot
growth.
Using Multiobjective Evolutionary
Algorithms to Assess Biological Simulation Models. In S.
Obayashi et al., Eds., EMO 2007: Proceedings of the Fourth
International Conference on Evolutionary Multi-Criterion
Optimization, Matsushima/Senda Japan, March 5-8, 2007.
Lecture Notes in Computer Science 4403, Springer-Verlag,
Berlin, pp 560-574. Defining the model assessment
problem as a problem in evolutionary computation optimization.
Further illustration of how to interrogate the model's Pareto
frontier to reveal the sources of structural deficiencies in a
model of branch shoot growth.
The use of multi-criteria assessment in
developing a process model. Ecological Modelling, 197:
320-330. Further illustration of the Pareto Optimal
Model Assessment Cycle applied to the assessment of a model
of branch shoot growth.
Multi-criteria inference for process
models: structural and parametric inference for a stochastic
model of feline hematopoiesis. 2004 Proceedings of the
American Statistical Association, Section on Statistics &
the Environment, Alexandria, VA: American Statistical
Association. Introducing the potential conflicts from
assessing (structural inference) a stochastic process model
following the standard statistical inference sequence based on
conditioning on adequacy of the model structure.
Multi-Criteria assessment of ecological
process models. Ecology. 80(2): 538 - 553.
Introduces the Pareto Optimal Model Assessment Cycle and
the illustrates the use of the Pareto frontier for model
assessment. Assessment of the canopy competition model WHORL
(Sorrensen-Cothern et al. 1993. Ecological Monographs).
Efficiently estimating salmon escapement
uncertainty using systematically sampled data. Pages 121-129 in
C. A. Woody, editor. Sockeye salmon ecology, evolution, and
management. American Fisheries Society, Symposium No. 54,
Bethesda, Maryland. Compares variance estimators for
non-replicated systematic samples,and non-replicated vs
replicated systematic sampling designs, for efficiently
estimating salmon escapement (based on tower count
observations). See associated R
code.
Smith et al. 2007
Harnessing farms and forests in the low carbon
economy: how to create, measure, and verify greenhouse gas.
G. R. Smith, B. A. McCarl, C. Li, J. H.
Reynolds, R. Hammerschlag, R. L. Sass, W. J. Parton, S. M.
Ogle, K. Paustian, J. Holtkamp, W. Barbour. Duke University
Press, ISBN-13: 978-0-8223-4168-0. 229 pp.
Statistical methods for
describing developmental milestones with censored data: Effects
of birth weight status and sex in neonatal pigtailed macaques.
American Journal of Primatology, 69: 1313-1324.
Detecting Specific Populations
in Mixtures. Proceedings of the 20th Lowell Wakefield Fisheries
Symposium: Genetics of Subpolar Fish and Invertebrates.
Environmental Biology of Fishes, 69:
233-243.