Prediction for spatio-temporal models with autoregression in errors
Prediction for spatio-temporal models with autoregression in errors
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DOI：
10.1080/10485252.2011.616893
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发表时间：
2012-01
影响因子：
1.2
通讯作者：
Hongxia Wang;Jinde Wang;B. Huang
中科院分区：
文献类型：
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作者：
Hongxia Wang;Jinde Wang;B. Huang
在各种环境研究中，都涉及到时空相关的数据，因此对时空相关性预测方法的需求日益增加，以提高预测的准确性。在本文中，我们提出了一个非参数迭代程序的时空模型具有特定的自相关结构。将空间数据的局部线性方法推广到时空局部线性模型，同时考虑了空间和时间特性。在较弱的条件下，建立了预测量的渐近正态性。仿真和实例研究的结果也表明，我们的预测性能优于传统的局部线性方法。
In various environmental studies spatio-temporal correlated data are involved, so there has been an increasing demand for spatio-temporal prediction methods that capture spatio-temporal correlation so as to improve the accuracy of prediction. In this paper we propose a nonparametric iteration procedure for spatio-temporal models with specific autocorrelation structures. We extended the local linear method for spatial data to spatio-temporal local linear models, taking both spatial and temporal characteristics into consideration. The asymptotic normality of the predictors is established under mild conditions. The results of a simulation and case study also show that our predictors perform better than the traditional local linear method.