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https://doi.org/10.1002/9781118866641.ch13

has been cited by the following article:

• TITLE: A Simulation Study of Hierarchical Bayesian Fusion Spatial Small Area Model for Binary Outcome under Spatial Misalignment

  AUTHORS: Kindie Fentahun Muchie, Anthony Kibira Wanjoya, Samuel Musili Mwalili

  KEYWORDS: Simulation, Small Area Estimation, Hierarchical Bayesian, Spatial Misalign-ment, Fusion Process

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.6, December 10, 2021

  ABSTRACT: Simulation (stochastic) methods are based on obtaining random samplesθ5from the desired distribution p(θ)and estimating the expectation of any function h(θ). 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. Small area estimation is a method for producing reliable estimates for small areas. Model based Bayesian small area estimation methods are becoming popular for their ability to combine information from several sources as well as taking account of spatial prediction of spatial data. In this study, detailed simulation algorithm is given and the performance of a non-trivial extension of hierarchical Bayesian model for binary data under spatial misalignment is assessed. Both areal level and unit level latent processes were considered in modeling. The process models generated from the predictors were used to construct the basis so as to alleviate the problem of collinearity between the true predictor variables and the spatial random process. The performance of the proposed model was assessed using MCMC simulation studies. The performance was evaluated with respect to root mean square error (RMSE), Mean absolute error (MAE) and coverage probability of corresponding 95% CI of the estimate. The estimates from the proposed model perform better than the direct estimate.