Journal Article

Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data

Kennedy, Daniel W., Jahan, Farzana, Duncan, Earl W., Mengersen, Kerrie L.

2022

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Bayes Theorem / Bayesian analysis / Bayesian statistical decision theory / Computer and Information Sciences / Coronaviruses / COVID-19 / COVID-19 - epidemiology / Data analysis / Data visualization / Datasets / Earth Sciences / Empirical analysis / Engineering and Technology / Humans / Infant mortality / Likelihood Functions / Mathematical models / Medicine and Health Sciences / Modelling / Normal Distribution / Physical Sciences / Research and Analysis Methods / Spatial Analysis / Spatial data / Survival analysis