Alleviating spatial confounding for areal data problems by displacing the geographical centroids

DOI10.1214/18-BA1123zbMath1421.62064OpenAlexW2891465577WikidataQ129223177 ScholiaQ129223177MaRDI QIDQ2316977

Erica Castilho Rodrigues, Renato M. Assunção, Marcos Oliveira Prates

Publication date: 7 August 2019

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1537258137

zbMATH Keywords

Bayesian statisticsspatial regressionareal dataspatial confoundingSPOCKgeographical centroids

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12) Applications of statistics to physics (62P35) Geostatistics (86A32) General questions in astronomy and astrophysics (85A04)

Related Items (8)

Restricted Spatial Regression Methods: Implications for Inference ⋮ On Deconfounding Spatial Confounding in Linear Models ⋮ A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications ⋮ A flexible Bayesian nonconfounding spatial model for analysis of dispersed count data ⋮ Bayesian spatial quantile modeling applied to the incidence of extreme poverty in Lima-Peru ⋮ Inducing high spatial correlation with randomly edge-weighted neighborhood graphs ⋮ Alleviating spatial confounding for areal data problems by displacing the geographical centroids ⋮ MSPOCK: alleviating spatial confounding in multivariate disease mapping models

Uses Software

• R
• WinBUGS
• GMRFLib
• CCA
• CARBayes
• ngspatial
• CCP

Cites Work

• Bayesian spatial models with a mixture neighborhood structure
• The importance of scale for spatial-confounding bias and precision of spatial regression estimators
• Bayesian image restoration, with two applications in spatial statistics (with discussion)
• Type S error for classical and Bayesian single and multiple comparison procedures
• Alleviating spatial confounding for areal data problems by displacing the geographical centroids
• The Bayesian group Lasso for confounded spatial data
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love
• Gaussian Markov Random Fields
• Approximate Inference in Generalized Linear Mixed Models
• On the Independence of k Sets of Normally Distributed Statistical Variables
• Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
• Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease‐Mapping Models
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