NONPARAMETRIC PREDICTION WITH SPATIAL DATA
提出一种基于谱密度函数规范分解的空间数据非参数预测算法，理论证明其渐近性质，蒙特卡洛模拟显示优于基于无限自回归表示的方法，并应用于洛杉矶房价预测。
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.