Usefulness of the Information Contained in the Prediction Sample for the Spatial Error Model
Usefulness of the Information Contained in the Prediction Sample for the Spatial Error Model
复制标题
预测样本中包含的信息对于空间误差模型的有用性
DOI：
10.1007/s11146-011-9345-9
复制
发表时间：
2012
影响因子：
1.9
通讯作者：
Takafumi Kato
中科院分区：
文献类型：
--
作者：
Kato;Takafumi;野田英雄・姜興起;橋本直樹;鈴木純;西埜晴久;橋本直樹;姜興起・野田英雄;Takafumi Kato;佐々木亘;橋本直樹;西埜晴久;野田英雄・姜興起;Takafumi Kato
最近的一项研究提出了一种估计方法，该方法使用自变量和预测样本位置的数据，并建议它可以改善估计和预测。这是一种不完整的数据方法，遵循沿着EM算法的路线沿着的迭代过程。本研究比较了这种方法与部分数据的方法，只使用数据的因变量和自变量和位置的估计样本。我们的Monte Carlo实验表明，除非估计和预测样本构成整个总体，并且数据生成模型用作数据拟合模型，否则不完全数据方法并不保证优于部分数据方法上级。
A recent study proposed an estimation approach that uses data on the independent variables and location for the prediction sample, and suggested that it may improve estimation and prediction. This is an incomplete data approach following an iterative process along the lines of the EM algorithm. The present study compares this approach with a partial data approach that uses only data on the dependent and independent variables and location for the estimation sample. Our Monte Carlo experiments show that unless the estimation and prediction samples constitute the whole population and the data generating model is used as the data fitting model, the incomplete data approach is not guaranteed to be superior to the partial data approach.