Evidence map›Paper›PMID 40857504›Full record

ArticleJournal of the American Statistical Association2024

Local signal detection on irregular domains with generalized varying coefficient models.

Chengzhu Zhang, Lan Xue, Yu Chen, Heng Lian, Annie Qu

Abstract read
PubMed Full text at PMC Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Chengzhu ZhangDepartment of Statistics, Oregon State University.
Lan XueDepartment of Statistics, Oregon State University.
Yu ChenDepartment of Statistics and Finance.
Heng LianCityU Shenzhen Research Institute, Shenzhen, China.
Annie QuDepartment of Statistics, University of California, Irvine.

Funding

Measurement error correction approaches to wearable device-based measures of physical activity and self-reported measures of dietary intake in obesity and type 2 diabetes researchR01DK132385 · NIDDK · TRUSTEES OF INDIANA UNIVERSITY · PI TEKWE, CARMEN D. · 2022 to 2025
$2.3M
SCH: Individualized learning and prediction for heterogeneous multimodal data from wearable devicesR01CA297869 · NCI · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI Yuqing Guo, Annie Qu · 2024 to 2026
$900k
NCI NIH HHS R01 CA297869NIDDK NIH HHS R01 DK132385
6 · The paper itself

Abstract

In spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful framework to accommodate spatial heterogeneity by allowing regression coefficients to vary in a given spatial domain. We propose a penalized bivariate spline method for detecting local signals in GSVCM. The key idea is to use bivariate splines defined on triangulation to approximate nonparametric varying coefficient functions and impose a local penalty on

Indexed as

Beijing housingBivariate splineModel confidence regionPenalized splineTriangulation

Identifiers

PMID40857504
PMCPMC12330956

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.