Model selection for geostatistical models

doi:10.1890/04-0576

Model selection for geostatistical models

2006-02-01

171

PRE

AI

J

Jennifer A. Hoeting *

DOI：10.1890/04-0576

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We consider the problem of model selection for geospatial data. Spatial correlation is often ignored in the selection of explanatory variables, and this can influence model selection results. For example, the importance of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often-used traditional approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also apply the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored. R software to implement the geostatistical model selection methods described in this paper is available in the Supplement.

Keyword:

AIC

geospatial data

kriging

matern autocorrelation function

MDL

orange-throated whiptail lizard abundance

Model selection for geostatistical models

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