Hierarchical Bayes Modeling of Survey-Weighted Small Area Proportions
Hierarchical Bayes Modeling of Survey-Weighted Small Area Proportions
复制标题
调查加权小面积比例的分层贝叶斯建模
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
G. Kalton
中科院分区：
文献类型：
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作者：
Benmei Liu;P. Lahiri;G. Kalton
本文报告的Monte Carlo模拟研究的结果，进行了比较的有效性，四个不同的分层贝叶斯小区域模型的生产状态估计的比例从分层简单随机样本的数据的基础上，从一个固定的有限人口。其中两个模型采用了通常的假设，即每个抽样小地区的调查加权比例呈正态分布，而且这一比例的抽样方差是已知的。其中一个模型使用线性连接模型，另一个使用逻辑连接模型。另外两个模型均采用logistic连接模型，并假设抽样方差未知。其中一个模型假设抽样模型为正态分布，而另一个模型假设贝塔分布。研究发现，对于所有四种模型，基于可信区间设计的有限总体状态比例的覆盖率明显偏离了构建区间时使用的95%标称水平。
The paper reports the results of a Monte Carlo simulation study that was conducted to compare the effectiveness of four different hierarchical Bayes small area models for producing state estimates of proportions based on data from stratified simple random samples from a fixed finite population. Two of the models adopted the commonly made assumptions that the survey weighted proportion for each sampled small area has a normal distribution and that the sampling variance of this proportion is known. One of these models used a linear linking model and the other used a logistic linking model. The other two models both employed logistic linking models and assumed that the sampling variance was unknown. One of these models assumed a normal distribution for the sampling model while the other assumed a beta distribution. The study found that for all four models the credible interval design-based coverage of the finite population state proportions deviated markedly from the 95 percent nominal level used in constructing the intervals.