Modeling Multivariate Spatial Dependencies Using Graphical Models.

Debangan Dey, Abhirup Datta, Sudipto Banerjee

Abstract read

In one paragraph

Article in The New England Journal of Statistics in Data Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Debangan DeyDepartment of Biostatistics, Johns Hopkins University, USA.

Abhirup DattaDepartment of Biostatistics, Johns Hopkins University, USA.

Sudipto BanerjeeDepartment of Biostatistics, University of California Los Angeles, USA.

Funding

Statistical methods for air-pollution studies using low-cost monitorsR01ES033739 · NIEHS · JOHNS HOPKINS UNIVERSITY · PI Abhirup Datta · 2022 to 2026

$1.3M

NIEHS NIH HHS R01 ES033739

6 · The paper itself

Abstract

Graphical models have witnessed significant growth and usage in spatial data science for modeling data referenced over a massive number of spatial-temporal coordinates. Much of this literature has focused on a single or relatively few spatially dependent outcomes. Recent attention has focused upon addressing modeling and inference for substantially large number of outcomes. While spatial factor models and multivariate basis expansions occupy a prominent place in this domain, this article elucidates a recent approach, graphical Gaussian Processes, that exploits the notion of conditional independence among a very large number of spatial processes to build scalable graphical models for fully model-based Bayesian analysis of multivariate spatial data.

Indexed as

Bayesian inferenceCovariance selectionGraphical Gaussian ProcessGraphical modelsMultivariate dependenciesSpatial process models

Identifiers

PMID37817840

DOI10.51387/23-nejsds47

PMCPMC10563032

What OpenQuestion holds

Textmetadata

LicenceTDM

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Registered trials

None linked

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.