Files and links (1)
CC BY V4.0, Open Access
Journal article
Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data
PloS one, Vol.17(5), Art. e0268130
2022
PMID: 35622835
Abstract
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.
Details
- Title
- Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data
- Authors/Creators
- Farzana Jahan (Author)Daniel W Kennedy (Author) - 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, AustraliaEarl W. Duncan (Author) - 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, AustraliaKerrie L. Mengersen (Author) - 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
- Publication Details
- PloS one, Vol.17(5), Art. e0268130
- Publisher
- PLOS
- Grants
- Bayesian learning for decision making in the big data era, 2019-06-14T18:04:43.803493, Australian Research Council (Australia, Canberra) - ARC
- Identifiers
- 991005575768207891
- Copyright
- © 2022 Jahan et al.
- Murdoch Affiliation
- College of Science, Technology, Engineering and Mathematics
- Language
- English
- Resource Type
- Journal article
Metrics
76 File views/ downloads
49 Record Views