Information criteria have been widely used in many quantitative applications as an effort to select the most appropriate model that describes well enough the unknown population behavior for a given dataset. Studies have shown that their performance depends on several elements and the selection of the best fitted model is not always the same for all criteria. For this purpose, this research evaluates the performance of the three most often used information criteria, such as the Akaike information criterion, the Bayesian information criterion and Hannan and Quinn information criterion, for selecting spatial processes, taking into account that the sample in spatial analysis is regarded as a realization of a spatial process that incorporates the spatial dependence between the observations. Using a Monte Carlo analysis for the three most frequently applied in practice spatial processes, such as the first-order spatial autoregressive process, SAR(1), the first-order spatial moving average process, SMA(1), and the mixed spatial autoregressive moving average process, SARMA(1, 1), this study finds that these information criteria can assist the analyst to select the true process, but their behavior depends on sample size as well as on the magnitude of the spatial parameters, leading occasionally to alternative competitive processes.

信息标准已在许多定量应用程序中广泛使用，以努力选择最合适的模型，该模型足够好地描述给定数据集的未知种群行为。研究表明，它们的性能取决于几个因素，对于所有标准，最佳拟合模型的选择并不总是相同的。为此，本研究评估了三个最常用的信息标准（如Akaike信息标准，贝叶斯信息标准以及Hannan和Quinn信息标准）在选择空间过程方面的性能，同时考虑了空间分析中的样本被认为是空间过程的一种实现，其中纳入了观测值之间的空间依赖性。