Small area estimation of poverty proportions under unit-level temporal binomial-logit mixed models
Small area estimation of poverty proportions under unit-level temporal binomial-logit mixed models
复制标题
单位级时间二项式-logit混合模型下贫困比例的小区域估计
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
L. Santamaría
中科院分区：
文献类型：
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作者：
T. Hobza;D. Morales;L. Santamaría
贫困比例是可以用单位一级二项式-logit混合模型解释的二分变量的平均值。连续两年贫困比例的变化是描述贫困演变的一个指标。本文应用一个单位层次的时间二项-logit混合模型估计贫困比例及其变化。模型参数估计的最大似然法的拉普拉斯近似的对数似然。的比例和变化的经验最佳预测（EBP）的计算和插件估计相比。EBP的均方误差由参数自助法估计。仿真实验研究了EBP和插件估计器的经验行为。应用估计贫困的比例和变化，在县的区域的瓦伦西亚，西班牙，给出。
Poverty proportions are averages of dichotomic variables that can be explained by unit-level binomial-logit mixed models. The change between the poverty proportions of two consecutive years is an indicator describing the evolution of poverty. This paper applies a unit-level temporal binomial-logit mixed model for estimating poverty proportions and their changes. The model parameters are estimated by the maximum likelihood method for the Laplace approximation of the loglikelihood. The empirical best predictors (EBP) of proportions and changes are calculated and compared with plug-in estimators. The mean squared error of the EBP is estimated by a parametric bootstrap. A simulation experiment is carried out to study the empirical behavior of the EBP and the plug-in estimators. An application to the estimation of poverty proportions and changes in counties of the region of Valencia, Spain, is given.