Accounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modeling

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Accounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modeling

Please use this identifier to cite or link to this item: http://hdl.handle.net/10355/9069

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dc.contributor.author Cressie, Noel A. C.
dc.contributor.author Calder, Catherine A., 1976-
dc.contributor.author Clark, James Samuel, 1957-
dc.contributor.author Ver Hoef, Jay M.
dc.contributor.author Wikle, Christopher K., 1963-
dc.contributor.other University of Missouri-Columbia. College of Arts and Sciences. Department of Statistics
dc.date.accessioned 2010-11-17T16:07:10Z
dc.date.available 2010-11-17T16:07:10Z
dc.date.issued 2009-04
dc.identifier.citation Ecological Applications, 19(3), 2009, pp. 553-570. en_US
dc.identifier.uri http://hdl.handle.net/10355/9069
dc.description Copyright by the Ecological Society of America. en_US
dc.description.abstract Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises. en_US
dc.language.iso en_US en_US
dc.publisher Ecological Society of America en_US
dc.relation.ispartof Statistics publications (MU)
dc.subject Bayesian modeling en_US
dc.subject empirical Bayes en_US
dc.subject spatial process en_US
dc.subject spatiotemporal process en_US
dc.subject.lcsh Bayesian statistical decision theory en
dc.subject.lcsh Spatial analysis en
dc.subject.lcsh Multilevel models (Statistics) en
dc.subject.lcsh Ecology -- Statistical methods en
dc.title Accounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modeling en_US
dc.type Article en_US


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