ArticleJournal of the American Statistical Association2025
Bayesian Geostatistics Using Predictive Stacking.
Article in Journal of the American Statistical Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Bayesian Spatiotemporal Wombling.Journal of the American Statistical Association · 2026Article
- Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.Allergy · 2026Review
- Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking.Environmetrics · 2026Article
- Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.Bayesian analysis · 2026Article
- Assessing spatial disparities: a Bayesian linear regression approach.Biostatistics (Oxford, England) · 2025Article
- Bayesian Data Sketching for Varying Coefficient Regression Models.Journal of machine learning research : JMLR · 2025Article
- Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models.Journal of machine learning research : JMLR · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct spatial predictions at arbitrary locations. We exploit analytically tractable posterior distributions for regression coefficients of predictors and the realizations of the spatial process conditional upon process parameters. We subsequently combine such inference by stacking these models across the range of values of the hyper-parameters. We devise stacking of means and posterior densities in a manner that is computationally efficient without resorting to iterative algorithms such as Markov chain Monte Carlo (MCMC) and can exploit the benefits of parallel computations. We offer novel theoretical insights into the resulting inference within an infill asymptotic paradigm and through empirical results showing that stacked inference is comparable to full sampling-based Bayesian inference at a significantly lower computational cost.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.