Hoeting Jennifer A, Davis Richard A, Merton Andrew A, Thompson Sandra E
Department of Statistics, Colorado State University, Fort Collins, Colorado 80523-1877, USA.
Ecol Appl. 2006 Feb;16(1):87-98. doi: 10.1890/04-0576.
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.
我们考虑地理空间数据的模型选择问题。在选择解释变量时，空间相关性常常被忽略，而这会影响模型选择结果。例如，当忽略空间相关性时，特定解释变量的重要性可能并不明显。为解决这个问题，我们考虑将赤池信息准则（AIC）应用于地质统计模型。我们在此背景下给出了AIC的启发式推导，并提供了模拟结果，表明在地质统计模型中使用AIC优于在选择解释变量时经常使用的忽略空间相关性的传统方法。这些想法通过一个蜥蜴丰度模型得到了进一步证明。我们还将最小描述长度（MDL）原则应用于地质统计模型的变量选择。同时也探讨了抽样设计对解释协变量选择的影响。本文所述地质统计模型选择方法的R软件可在补充材料中获取。
