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Uncertainty in spatially predicted covariates: is it ignorable?


Foster, SD and Shimadzu, H and Darnell, R, Uncertainty in spatially predicted covariates: is it ignorable?, Journal of the Royal Statistical Society: Series C. Applied Statistics, 61, (4) pp. 637-652. ISSN 1467-9876 (2012) [Refereed Article]

Copyright Statement

2012 Royal Statistical Society

DOI: doi:10.1111/j.1467-9876.2011.01030.x


In ecology, a common form of statistical analysis relates a biological variable to variables that delineate the physical environment, typically by fitting a regression model or one of its extensions. Unfortunately, the biological data and the physical data are frequently obtained from separate sources of data. In such cases there is no guarantee that the biological and physical data are co-located and the regression model cannot be used. A common and pragmatic solution is to predict the physical variables at the locations of the biological variables and then to use the predictions as if they were observations. We show that this procedure can cause potentially misleading inferences and we use generalized linear models as an example. We propose a Berkson error model which overcomes the limitations. The differences between using predicted covariates and the Berkson error model are illustrated by using data from the marine environment, and a simulation study based on these data.

Item Details

Item Type:Refereed Article
Keywords:Berkson error, ecological modelling, geostatistics, measurement error
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Stochastic analysis and modelling
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Foster, SD (Dr Scott Foster)
ID Code:118989
Year Published:2012
Web of Science® Times Cited:23
Deposited By:Ecology and Biodiversity
Deposited On:2017-07-25
Last Modified:2017-08-28

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