This paper proposes a spatial k-nearest neighbor method for nonparametric prediction of real-valued spatial data and supervised classification for categorical spatial data. The proposed method is based on a double nearest neighbor rule which combines two kernels to control the distances between observations and locations. It uses a random bandwidth in order to more appropriately fit the distributions of the covariates. The almost complete convergence with rate of the proposed predictor is established and the almost sure convergence of the supervised classification rule was deduced. Finite sample properties are given for two applications of the k-nearest neighbor prediction and classification rule to the soil and the fisheries datasets
翻译:本文建议了一种空间公里近邻法,用于对实际价值空间数据进行不参数预测和对绝对空间数据进行监督分类。拟议方法基于一种双近近地规则,将两个内核结合起来,以控制观测和地点之间的距离。它使用随机带宽,以更恰当地适应共变分布。与拟议预测器的速率几乎完全一致,并推断出受监督分类规则几乎可以肯定的趋同。对于K最近邻预测和分类规则在土壤和渔业数据集中的两种应用,给出了精度样本特性。