论文标题
使用空间数据的非参数预测
Nonparametric prediction with spatial data
论文作者
论文摘要
我们基于光谱密度函数的规范分解，描述了用于空间数据的（非参数）预测算法。我们提供了理论上的结果，表明预测变量具有理想的渐近性质。在一项蒙特卡洛研究中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力学的无限AR表示，将我们的算法与竞争对手的非参数方法进行了比较。最后，我们运用我们的方法来预测洛杉矶的房价。
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.
