A Bayesian model unifies SAR and DAGAR spatial autoregressions with temporal AR into a GMRF framework for censored and missing areal data, outperforming ad-hoc imputations in simulations and applied to Beijing CO concentrations.
Spatial disease mapping using directed acyclic graph auto-regressive (DAGAR) models
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A Unified Spatiotemporal Framework for Modeling Censored and Missing Areal Responses
A Bayesian model unifies SAR and DAGAR spatial autoregressions with temporal AR into a GMRF framework for censored and missing areal data, outperforming ad-hoc imputations in simulations and applied to Beijing CO concentrations.