Bayesian Regression Analysis for Dependent Data with an Elliptical Shape.

Yian Yu, Long Tang, Kang Ren, Zhonglue Chen, Shengdi Chen, Jianqing Shi

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2024. 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

6 authors.

Yian YuDepartment of Statistics and Data Science, College of Science, Southern University of Science and Technology, Shenzhen 518055, China.

Long TangDepartment of Statistics and Data Science, College of Science, Southern University of Science and Technology, Shenzhen 518055, China.

Kang RenHUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan 430074, China.

Zhonglue ChenHUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan 430074, China.ORCID 0000-0001-7037-2481

Shengdi ChenDepartment of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

Jianqing ShiDepartment of Statistics and Data Science, College of Science, Southern University of Science and Technology, Shenzhen 518055, China.ORCID 0000-0002-2924-1137

Funding

National Key R&D Program of China 2023YFA1011400National Natural Science Foundation of China No.12271239Shenzhen Fundamental Research Program JCYJ20220818100602005 No.20220111

6 · The paper itself

Abstract

This paper proposes a parametric hierarchical model for functional data with an elliptical shape, using a Gaussian process prior to capturing the data dependencies that reflect systematic errors while modeling the underlying curved shape through a von Mises-Fisher distribution. The model definition, Bayesian inference, and MCMC algorithm are discussed. The effectiveness of the model is demonstrated through the reconstruction of curved trajectories using both simulated and real-world examples. The discussion in this paper focuses on two-dimensional problems, but the framework can be extended to higher-dimensional spaces, making it adaptable to a wide range of applications.

Indexed as

ellipsefunctional regression modelGaussian processnonlinear effectsshape constraintsvon Mises–Fisher distribution

Identifiers

PMID39766700

DOI10.3390/e26121072

PMCPMC11675188

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

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.