Empirical Bayes Small-Area Estimation Using Logistic Regression Models and Summary Statistics
提出一种基于二阶泰勒展开的经验贝叶斯方法，仅需局部区域的汇总统计量即可对非线性模型进行小区域参数预测，并用美国人口普查数据验证了方法有效性。
Many of the available methods for estimating small-area parameters are model-based approaches in which auxiliary variables are used to predict the variable of interest. For models that are nonlinear, prediction is not straightforward. MacGibbon and Tomberlin and Farrell, MacGibbon, and Tomberlin have proposed approaches that require microdata for all individuals in a small area. In this article, we develop a method, based on a second-order Taylor-series expansion to obtain model-based predictions, that requires only local-area summary statistics for both continuous and categorical auxiliary variables. The methodology is evaluated using data based on a U.S. Census.