小区域估计中的SEBLUP估计方法被引量：1

SEBLUP Method for Small Area Estimation

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摘要空间相关的经验最优线性无偏预测(SEBLUP)模型是指区域随机效应相关的线性混合模型,而模型中的参数估计方法是当前研究的重要问题之一。本文给出了模型中参数的极大似然(ML)估计过程,并通过仿真模拟,实验结果显示用ML估计方法估计SEBLUP模型的参数的条件下,SEBLUP估计方法处理空间相关的小区域数据效果较好。 Spatial Empirical Best Linear Unbiased Prediction is a spatial linear mixed model. Parameter estimation method is one significant study. In this paper, maximum likelihood （ML） estimation process of parameters was proposed in detail. And the simulation shows that ML estimation is feasible, and the SEBLUP method dealing with data of spatial small area is more effective.

作者刘燕玉

机构地区贵州大学理学院

出处《贵州大学学报（自然科学版）》 2013年第6期13-16,共4页 Journal of Guizhou University:Natural Sciences

基金全国统计科研计划项目资助(2012LZ054)

关键词小区域估计空间相关线性混合模型 SEBLUP 极大似然估计 small area estimation spatial correlation linear mixed model SEBLUP maximum likelihood estimation

分类号 O212.1 [理学—概率论与数理统计]

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