School of Mathematical Sciences, ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), QUT Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia.
PLoS One. 2022 May 27;17(5):e0268130. doi: 10.1371/journal.pone.0268130. eCollection 2022.
Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of spatial BEL models using two popular conditional autoregressive (CAR) priors, namely BYM and Leroux priors. The performance of the proposed models is compared with their parametric counterparts and with existing spatial BEL models using independent Gaussian priors and generalised Moran basis priors. The models are applied to two benchmark spatial datasets, simulation study and COVID-19 data. The results indicate promising opportunities for these models to capture new insights into spatial data. Specifically, the spatial BEL models outperform the parametric spatial models when the underlying distributional assumptions of data appear to be violated.
贝叶斯经验似然（BEL）模型作为完全参数模型的一种有吸引力的替代方法，越来越受到关注。然而，它们最近才被应用于小区域估计的空间数据分析。本研究考虑了使用两种流行的条件自回归（CAR）先验，即 BYM 和 Leroux 先验，来开发空间 BEL 模型。通过使用独立高斯先验和广义 Moran 基先验，将所提出的模型与参数化模型以及现有的空间 BEL 模型进行了比较。这些模型应用于两个基准空间数据集、模拟研究和 COVID-19 数据。结果表明，这些模型有希望为捕捉空间数据的新见解提供机会。具体来说，当数据的基础分布假设似乎被违反时，空间 BEL 模型优于参数化空间模型。
