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首页> 外文期刊>Open Journal of Statistics >A Simulation Study of Hierarchical Bayesian Fusion Spatial Small Area Model for Binary Outcome under Spatial Misalignment
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A Simulation Study of Hierarchical Bayesian Fusion Spatial Small Area Model for Binary Outcome under Spatial Misalignment

机译：空间未对准下二元成果分层贝叶斯融合空间小区模型的仿真研究

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Simulation (stochastic) methods are based on obtaining random samples? θ ~( 5 ) ? ? from the desired distribution p ( θ ) ? and estimating the expectation of any function h ( θ ) . Simulation methods can be used for high-dimensional dis tributions, 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 corres ponding 95% CI of the estimate. The estimates from the proposed model perform better than the direct estimate.
机译：仿真（随机）方法基于获取随机样本？ ＆amp;θ; 〜（5）？还从所需的分布p（＆amp; theta;）？并估计任何功能H（＆amp;θ）的期望。仿真方法可用于高维解划，并且有一般的算法适用于各种型号。马尔可夫链Monte Carlo（MCMC）方法对于在小区估计中为普通等级模型制作贝叶斯推理的方法很重要。小区估计是一种为小区产生可靠估计的方法。基于模型的贝叶斯小区估计方法正在成为他们将信息与多种来源的信息以及考虑空间数据的空间预测的流行。在该研究中，给出了详细的仿真算法，并评估了在空间未对准下的二进制数据中分层贝叶斯模型的非琐碎扩展的性能。在建模中考虑了面积水平和单位水平潜在过程。从预测器产生的过程模型用于构建基础，以便缓解真正的预测器变量与空间随机过程之间的共同性问题。使用MCMC仿真研究评估所提出的模型的性能。对根均方误差（RMSE），平均绝对误差（MAE）进行评估的性能，以及估计探测的95％CI的覆盖概率。来自拟议模型的估计比直接估计更好。

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