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.
Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.