A Quasi-Empirical Bayes Method for Small Area Estimation
提出一种仅基于条件独立分层均值和方差函数的准经验贝叶斯方法，用于小区域估计，并应用于华盛顿州各县癌症化疗利用率估计。
Abstract This article develops an empirical Bayes-type approach for small area estimation based only on the specification of a set of conditionally independent hierarchical mean and variance functions describing the first two moments of the process generating the data. The objective of the analysis is to draw inference about the intermediate or the random means. We combine the quasi-likelihood functions to construct the quasi-posterior density of the random means conditional on the data and the marginal parameters. We describe a method for estimating the marginal parameters that are then substituted in the quasi-posterior density of the random means. The empirical quasi-posterior density so derived is used to obtain the quasi-empirical Bayes estimates of the random parameters. We apply the methodology to estimate the utilization rates of cancer chemotherapy for the selected counties in the state of Washington. Key Words: Estimating equationsGeneralized EM algorithmJackknifeMarginal likelihoodMethod of moments