论文标题
非平稳空间建模
Non-Stationary Spatial Modeling
论文作者
论文摘要
标准的地统计模型假定平稳性并依靠变量图模型来说明观察到的数据中的空间依赖性。在某些情况下，这种假设显然违反了整个抽样区域的空间依赖结构是恒定的。我们提出了一个空间模型，该模型允许空间依赖结构随着位置的函数而变化。与以前不考虑这种非平稳性规范不确定性的公式不同（例如Sampson和Guttorp（1992）），我们开发了一个分层模型，该模型可以将这种不确定性纳入结果推论。非平稳的空间依赖性是通过建设性的“过程卷卷”方法来解释的，该方法可确保所得的协方差结构有效。我们将此方法应用于有毒废物修复中的示例。
Standard geostatistical models assume stationarity and rely on a variogram model to account for the spatial dependence in the observed data. In some instances, this assumption that the spatial dependence structure is constant throughout the sampling region is clearly violated. We present a spatial model which allows the spatial dependence structure to vary as a function of location. Unlike previous formulations which do not account for uncertainty in the specification of this non-stationarity (eg. Sampson and Guttorp (1992)), we develop a hierarchical model which can incorporate this uncertainty in the resulting inference. The non-stationary spatial dependence is explained through a constructive "process-convolution" approach, which ensures that the resulting covariance structure is valid. We apply this method to an example in toxic waste remediation.
