MSPOCK: alleviating spatial confounding in multivariate disease mapping models
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Publication:2084438
DOI10.1007/s13253-021-00451-5OpenAlexW3153591389MaRDI QIDQ2084438
Douglas R. M. Azevedo, Dipankar Bandyopadhyay, Marcos Oliveira Prates
Publication date: 18 October 2022
Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s13253-021-00451-5
Bayesianvariance inflationspatial confoundingareal modelingrespiratory system cancershared componentsSPOCK
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- spBayes
- Understanding predictive information criteria for Bayesian models
- Bayesian spatial models with a mixture neighborhood structure
- Constrained Delaunay triangulations
- Bayesian image restoration, with two applications in spatial statistics (with discussion)
- Markov chain Monte Carlo with the integrated nested Laplace approximation
- Inference from iterative simulation using multiple sequences
- Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models
- 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
- A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models
- A Bivariate Bayes Method for Improving the Estimates of Mortality Rates With a Twofold Conditional Autoregressive Model
- Towards joint disease mapping
- Issues in the mapping of two diseases
- Bayesian Detection of Clusters and Discontinuities in Disease Maps
- Bayesian Measures of Model Complexity and Fit
- Gaussian Markov Random Fields
- Approximate Inference in Generalized Linear Mixed Models
- A Shared Component Model for Detecting Joint and Selective Clustering of Two Diseases
- Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
- Bias Correction With Jackknife, Bootstrap, and Taylor Series
- Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease‐Mapping Models
- Bayesian Disease Mapping
- Proper multivariate conditional autoregressive models for spatial data analysis
- A test for the serial independence of residuals
- Structural Equation Models for Dealing With Spatial Confounding
- Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification
