We describe a (nonparametric) prediction algorithm for spatial data, based on a canonical factorization of the spectral density function. We provide theoretical results showing that the predictor has desirable asymptotic properties. Finite sample performance is assessed in a Monte Carlo study that also compares our algorithm to a rival nonparametric method based on the infinite $AR$ representation of the dynamics of the data. Finally, we apply our methodology to predict house prices in Los Angeles.

中文翻译：

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空间数据的非参数预测

我们描述了一种基于谱密度函数的规范分解的空间数据（非参数）预测算法。我们提供的理论结果表明预测器具有理想的渐近特性。蒙特卡罗研究评估了有限样本性能，该研究还将我们的算法与基于数据动态的无限$AR$表示的非参数方法进行比较。最后，我们应用我们的方法来预测洛杉矶的房价。