Hierarchical Bayesian Estimation of Small Area Means Using Complex Survey Data

TL;DRAbstract

In survey data analysis, there are two main approaches -design-based and modelbased-for making inferences for different characteristics of the population. A designbased approach tends to produce unreliable estimates for small geographical regions or cross classified demographic regions due to the small sample sizes. Moreover, when there are no samples available in those areas, a design-based method cannot be used. In the case of estimating population characteristics for a small area, model-based methods are used. They provide a flexible modeling method that can incorporate relevant information from similar areas and external databases. To provide suitable estimates, many model building techniques, both frequentist and Bayesian, have been developed, and when the model-based method makes an explicit use of prior distributions on the hyperparameters, inference can be carried out in the Bayesian paradigm. For estimating small area proportions, mixed models are often used because of the fle

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