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dc.contributor.authorCressie, Noel A. C.
dc.contributor.authorCalder, Catherine A., 1976-
dc.contributor.authorClark, James Samuel, 1957-
dc.contributor.authorVer Hoef, Jay M.
dc.contributor.authorWikle, Christopher K., 1963-
dc.contributor.otherUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Statistics
dc.descriptionCopyright by the Ecological Society of America.en_US
dc.description.abstractAnalyses 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.identifier.citationEcological Applications, 19(3), 2009, pp. 553-570.en_US
dc.publisherEcological Society of Americaen_US
dc.relation.ispartofStatistics publications (MU)
dc.subjectBayesian modelingen_US
dc.subjectempirical Bayesen_US
dc.subjectspatial processen_US
dc.subjectspatiotemporal processen_US
dc.subject.lcshBayesian statistical decision theoryen
dc.subject.lcshSpatial analysisen
dc.subject.lcshMultilevel models (Statistics)en
dc.subject.lcshEcology -- Statistical methodsen
dc.titleAccounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modelingen_US

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