Evidence map›Paper›PMID 39926184›Full record

ArticleJournal of applied statistics2025

Inference and diagnostics for censored linear regression model with skewed generalized

Chengdi Lian, Yaohua Rong, Jinwen Liang, Ruijie Guan, Weihu Cheng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied 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

5 authors.

Chengdi LianSchool of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0003-0208-6728
Yaohua RongSchool of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, People's Republic of China.
Jinwen LiangSchool of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, People's Republic of China.
Ruijie GuanSchool of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, People's Republic of China.
Weihu ChengSchool of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, some interested data can be recorded only if the values fall within an interval range, and the responses are often subject to censoring. Attempting to perform effective statistical analysis with censored, especially heavy-tailed and asymmetric data, can be difficult. In this paper, we develop a novel linear regression model based on the proposed skewed generalized

Indexed as

62J0562J2062N0162N02Censored regression modelEM-type algorithmsinfluence analysisresidual analysisskewed generalized t distribution

Identifiers

PMID39926184
PMCPMC11800352

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.