A flexible Bayesian nonconfounding spatial model for analysis of dispersed count data.
INLA
count data
non-confounding spatial modeling
over-dispersion
under-dispersion
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
revised:
14
11
2021
received:
20
05
2021
accepted:
16
11
2021
pubmed:
6
1
2022
medline:
20
4
2022
entrez:
5
1
2022
Statut:
ppublish
Résumé
In employing spatial regression models for counts, we usually meet two issues. First, the possible inherent collinearity between covariates and the spatial effect could lead to misleading inferences. Second, real count data usually reveal over- or under-dispersion where the classical Poisson model is not appropriate to use. We propose a flexible Bayesian hierarchical modeling approach by joining nonconfounding spatial methodology and a newly reconsidered dispersed count modeling from the renewal theory to control the issues. Specifically, we extend the methodology for analyzing spatial count data based on the gamma distribution assumption for waiting times. The model can be formulated as a latent Gaussian model, and consequently, we can carry out the fast computation by using the integrated nested Laplace approximation method. We examine different popular approaches for handling spatial confounding and compare their performances in the presence of dispersion. Two real applications from a crime study against women in India as well as stomach cancer incidences in Slovenia motivate the suggested methods. We also perform a simulation study to understand the proposed approach's merits better. Supplementary Materials for this article are available.
Identifiants
pubmed: 34985802
doi: 10.1002/bimj.202100157
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
758-770Informations de copyright
© 2022 Wiley-VCH GmbH.
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