Abstract
A Simulation Study of Hierarchical Bayesian Fusion Spatial Small Area Model for Binary Outcome under Spatial Misalignment
Highlights
Simulation methods are based on obtaining random samples of the parameters of interest from the desired distribution and estimating the expectation of any function of the parameters
Simulation methods can be used for high-dimensional distributions, and there are general algorithms which work for a wide variety of models
Markov chain Monte Carlo (MCMC) methods have been important in making Bayesian inference practical for generic hierarchical models in small area estimation
Summary
Simulation (stochastic) methods are based on obtaining random samples of the parameters of interest from the desired distribution and estimating the expectation of any function of the parameters. It can be used for high-dimensional distributions, and there are general algorithms which work for a wide variety of models; where necessary, more efficient computation can be obtained by combining these general ideas with tailored simulation methods, deterministic methods, and distributional approximations. To improve accuracy and reliability, a variety of estimators have been developed that combine survey data for the target small areas with data from outside the survey, frequently related to recent censuses and current administrative data. Even when a model is misspecified, [5] that use working models, in which a model is specified but desirable design based attributes are preserved, are called model assisted estimators
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