Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models

DOI10.1214/19-BA1177zbMath1435.62319arXiv1704.07848MaRDI QIDQ2290712

Abhirup Datta, Leiwen Gao, James S. Hodges, Sudipto Banerjee

Publication date: 29 January 2020

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

Full work available at URL: https://arxiv.org/abs/1704.07848

zbMATH Keywords

Bayesian inferencedirected acyclic graphsdisease mappingspatial autoregressionareal data

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Applications of graph theory (05C90)

Related Items

A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data, A skew-normal spatial simultaneous autoregressive model and its implementation, Inducing high spatial correlation with randomly edge-weighted neighborhood graphs, Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models, Size estimation of key populations in the HIV epidemic in eSwatini using incomplete and misaligned capture-recapture data, MSPOCK: alleviating spatial confounding in multivariate disease mapping models

Uses Software

• glasso
• spBayes
• ngspatial

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sparse inverse covariance estimation with the graphical lasso
• Optimal rates of convergence for covariance matrix estimation
• Covariance regularization by thresholding
• Operator norm consistent estimation of large-dimensional sparse covariance matrices
• Image analysis with partially ordered Markov models.
• Interpolation of spatial data. Some theory for kriging
• A close look at the spatial structure implied by the CAR and SAR models.
• Towards a multidimensional approach to Bayesian disease mapping
• Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models
• Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis
• Regularized estimation of large covariance matrices
• High-dimensional graphs and variable selection with the Lasso
• Nonparametric estimation of large covariance matrices of longitudinal data
• Training Products of Experts by Minimizing Contrastive Divergence
• First-order intrinsic autoregressions and the de Wijs process
• Approximating Likelihoods for Large Spatial Data Sets
• Bayesian Measures of Model Complexity and Fit
• Positive-Definite ℓ₁-Penalized Estimation of Large Covariance Matrices
• Bayesian Analysis of Agricultural Field Experiments
• Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
• Neighborhood Dependence in Bayesian Spatial Models
• Generalized Thresholding of Large Covariance Matrices
• A general modelling framework for multivariate disease mapping
• Proper multivariate conditional autoregressive models for spatial data analysis
• ON STATIONARY PROCESSES IN THE PLANE