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.

翻译：我们描述空间数据的(非参数)预测算法,其依据是光谱密度函数的明度乘数。我们提供了理论结果,表明预测器具有可取的无症状特性。蒙特卡洛的一项研究评估了微量样本性能,该研究还将我们的算法与基于数据动态无穷的AR表示的相对非参数性方法进行比较。最后,我们运用了我们的方法来预测洛杉矶的房价。