Analytic and bootstrap approximations of prediction errors under a multivariate Fay-Herriot model
Analytic and bootstrap approximations of prediction errors under a multivariate Fay-Herriot model
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多元 Fay-Herriot 模型下预测误差的解析和引导近似
DOI：
10.1016/j.csda.2008.04.031
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发表时间：
2008
影响因子：
1.8
通讯作者：
L. Santamaría
中科院分区：
文献类型：
--
作者：
W. González;M. Lombardía;I. Molina;D. Morales;L. Santamaría
解决了基于多元 Fay-Herriot 模型的小面积量向量的预测。为此，使用目标向量的经验最佳线性无偏预测器（EBLUP），其中模型参数通过两种基于矩的不同方法估计。多维 EBLUP 的平均叉积误差矩阵通过分析和狂野引导方法进行近似，该方法产生直接和偏差校正的引导估计量。模拟研究比较了自举估计量和解析近似的小样本特性，包括缺乏正态性的情况下的比较。最后，研究了引导程序稳定所需的重复次数。
The prediction of vectors of small area quantities based on a multivariate Fay–Herriot model is addressed. For this, an empirical best linear unbiased predictor (EBLUP) of the target vector is used, where the model parameters are estimated by two different methods based on moments. The mean cross product error matrix of the multidimensional EBLUP is approximated both analytically and by a wild bootstrap method that yields direct and bias-corrected bootstrap estimators. A simulation study compares the small sample properties of the bootstrap estimators and the analytical approximation, including a comparison under lack of normality. Finally, the number of replicates needed for the bootstrap procedures to get stabilized are studied.