Evidence map›Paper›PMID 39006311›Full record

ArticleJournal of the American Statistical Association2024

Bayesian Modeling with Spatial Curvature Processes.

Aritra Halder, Sudipto Banerjee, Dipak K Dey

Abstract read
In one paragraph

Article in Journal of the American Statistical Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Bayesian Spatiotemporal Wombling.Journal of the American Statistical Association · 2026
    Article
  2. Investigating Spatial Dynamics in Spatial Omics Data with StarTrail.Journal of the American Statistical Association · 2026
    Article
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.

Aritra HalderDepartment of Biostatistics, Drexel University, Philadelphia, PA, USA.
Sudipto BanerjeeDepartment of Biostatistics, University of California, Los Angeles, CA, USA.
Dipak K DeyDepartment of Statistics, University of Connecticut, Storrs, CT, USA.

Funding

Bayesian Modeling and Inference for High-Dimensional Disease Mapping and Boundary Detection"R01GM148761 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Sudipto Banerjee · 2023 to 2026
$1.2M
NIGMS NIH HHS R01 GM148761
6 · The paper itself

Abstract

Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insights into the nature of latent dependence within the studied response. We develop Bayesian modeling and inference for rapid changes on the response surface to assess directional curvature along a given trajectory. Such trajectories or curves of rapid change, often referred to as

Indexed as

Bayesian modelingDirectional CurvatureGaussian ProcessesWombling

Identifiers

PMID39006311
PMCPMC11238907

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.