Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/105261
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Type: Journal article
Title: Prediction intervals: placing real bounds on regression-based allometric estimates of biomass
Author: Ward, P.
Citation: Biometrical Journal: journal of mathematical methods in biosciences, 2015; 57(4):695-711
Publisher: Wiley-VCH Verlag
Issue Date: 2015
ISSN: 0323-3847
1521-4036
Statement of
Responsibility: 
Peter J. Ward
Abstract: Biomass allometry studies routinely assume that regression models can be applied across species and sites, and that goodness of fit of a regression model to its derivation dataset indicates both the relevance of the model to a new dataset and the likely error. Assuming that a model is relevant for a new sample, a prediction interval is a useful error measure for stand mass. Prediction coverage tests whether the model and hence the interval are appropriate in the new sample. Data for three similar shrubby species from four similar sites were combined in various ways to test the impact of varying levels of biodiverse heterogeneity on the performance of the four models most commonly used in published biomass studies. No one model performed consistently well predicting new data, and validation checks were not good indicators of prediction coverage. The highly variable results suggest that the common models might contain insufficient variables. Euclidean distance was used to quantify the relative similarity of samples as a possible means of estimating prediction coverage; it proved unsuccessful with these data.
Keywords: Biomass allometry; prediction coverage; prediction interval; regression error
Rights: © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
DOI: 10.1002/bimj.201400070
Appears in Collections:Aurora harvest 8
Earth and Environmental Sciences publications

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