Spatio-Temporal Models in Small Area Estimation
Spatio-Temporal Models in Small Area Estimation
复制标题
小区域估计中的时空模型
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
D. Kundu
中科院分区：
文献类型：
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作者：
Bharat Singh;G. K. Shukla;D. Kundu
针对小面积估计问题，提出了一种通用混合效应模型框架下的空间回归模型。小区域间共同的非相关系数使小区域估计值得到改善。人们发现，在由于外生变量导致小面积估计几乎没有改善的情况下，它非常有用。对经验线性无偏预测器（EBLUP）的均方误差（MSE）的二阶或二阶近似也得到了解决。利用卡尔曼滤波方法，提出了一种时空模型。在这种情况下，也得到了EBLUP的MSE的二阶近似。作为一个案例研究，来自印度政府统计和计划实施部国家抽样调查组织（NSSO）的时间序列月人均消费支出（MPCE）数据被用于验证模型。
A spatial r egression model in a general mixed ef fects model framework has been proposed for the small ar ea estimation problem. A common a utocorrelation pa rameter across the small areas has r esulted in the improvement of the small area estimates. It has been found to be very useful in the cases where there is little improvement in the small area estimates due to the exogenous variables. A second or der approximation to the mean squared e rror (MSE) of the empirical be st linear unbiased predictor (EBLUP) has also been worked out. Using the Kalman filtering approach, a spatial temporal model has been proposed. I n this case also, a second order approximation to the MSE of the EBLUP has been obtained. A s a case study, the time series monthly per capita consumption expenditure (MPCE) data from the National Sa mple Survey Organisation (NSSO) of the Ministry of Statistics and Programme Implementation, Government of India, have been used for the validation of the models.