Evidence map›Paper›PMID 40895827›Full record

ArticleJournal of nonparametric statistics2025

Nonparametric Density Estimation for Data Scattered on Irregular Spatial Domains: A Likelihood-Based Approach Using Bivariate Penalized Spline Smoothing.

Kunal Das, Shan Yu, Guannan Wang, Li Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of nonparametric statistics, 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

4 authors.

Kunal DasDepartment of Statistics, Iowa State University, Ames, IA, 50011, USA.
Shan YuDepartment of Statistics, University of Virginia, Charlottesville, VA, 22901, USA.
Guannan WangDepartment of Mathematics, William & Mary, Williamsburg, VA, 23187, USA.
Li WangDepartment of Statistics, George Mason University, Fairfax, VA 22030, USA.

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

Accurately estimating data density is crucial for making informed decisions and modeling in various fields. This paper presents a novel nonparametric density estimation procedure that utilizes bivariate penalized spline smoothing over triangulation for data scattered over irregular spatial domains. Our likelihood-based approach incorporates a regularization term addressing the roughness of the logarithm of density using a second-order differential operator. We establish the asymptotic convergence rate of the proposed density estimator in terms of the

Indexed as

62G07Bivariate splinesComplex domainDensity estimationPenalized splinesTriangulations

Identifiers

PMID40895827
PMCPMC12393688

What OpenQuestion holds

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Registered trials

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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.