Evidence map›Paper›PMID 40980191›Full record

ArticleJournal of the American Statistical Association2025

Estimation and Inference of Quantile Spatially Varying Coefficient Models Over Complicated Domains.

Myungjin Kim, Lily Wang, Huixia Judy Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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. 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.

Myungjin KimAssistant Professor, Department of Statistics, KNU G-LAMP Project Group, KNU Institute of Basic Sciences, Kyungpook National University, Daegu, 41566, South Korea.
Lily WangProfessor, Department of Statistics, George Mason University, Fairfax, VA 22030.
Huixia Judy WangProfessor, Department of Statistics, The George Washington University, Washington, DC 20052.

Funding

SCH: Novel and Interpretable Statistical Learning for Brain Images in AD/ADRDsR01AG085616 · NIA · GEORGE MASON UNIVERSITY · PI XIAO SONG, GuanNan Wang · 2023 to 2026
$1.1M
NIA NIH HHS R01 AG085616
6 · The paper itself

Abstract

This paper presents a flexible quantile spatially varying coefficient model (QSVCM) for the regression analysis of spatial data. The proposed model enables researchers to assess the dependence of conditional quantiles of the response variable on covariates while accounting for spatial nonstationarity. Our approach facilitates learning and interpreting heterogeneity in spatial data distributed over complex or irregular domains. We introduce a quantile regression method that utilizes bivariate penalized splines in triangulation to estimate unknown functional coefficients. We establish the

Indexed as

Alternating direction method of multiplierBivariate penalized splineConformal predictionNonparametric quantile regressionTriangulation

Identifiers

PMID40980191
PMCPMC12445109

What OpenQuestion holds

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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.