Evidence map›Paper›PMID 38625258›Full record

ArticleAnnals of data science2023

A New Extension of the Topp-Leone-Family of Models with Applications to Real Data.

Mustapha Muhammad, Lixia Liu, Badamasi Abba, Isyaku Muhammad, Mouna Bouchane, Hexin Zhang, Sani Musa

Abstract read
In one paragraph

Article in Annals of data science, 2023. 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

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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

7 authors.

Mustapha MuhammadDepartment of Mathematics, Guangdong University of Petrochemical Technology, Maoming, 525000 China.ORCID 0000-0003-0329-5898
Lixia LiuSchool of Mathematical Sciences, Hebei Normal University, Shijiazhuang, 050024 People's Republic of China.
Badamasi AbbaSchool of Mathematics and Statistics, Central South University, Changsha, China.
Isyaku MuhammadSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731 China.
Mouna BouchaneKey Laboratory of Augmented Reality College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, China.
Hexin ZhangSchool of Mathematical Sciences, Hebei Normal University, Shijiazhuang, 050024 People's Republic of China.
Sani MusaDepartment of Mathematics and Computer Science, Sule Lamido University, Kafin-Hausa, Jigawa Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, we proposed a new extension of the Topp-Leone family of distributions. Some important properties of the model are developed, such as quantile function, stochastic ordering, model series representation, moments, stress-strength reliability parameter, Renyi entropy, order statistics, and moment of residual life. A particular member called new extended Topp-Leone exponential (NETLE) is discussed. Maximum likelihood estimation (MLE), least-square estimation (LSE), and percentile estimation (PE) are used for the model parameter estimation. Simulation studies were conducted using NETLE to assess the MLE, LSE, and PE performance by examining their bias and mean square error (MSE), and the result was satisfactory. Finally, the applications of the NETLE to two real data sets are provided to illustrate the importance of the NETLG families in practice; the data sets consist of daily new deaths due to COVID-19 in California and New Jersey, USA. The new model outperformed many other existing Topp-Leone's and exponential related distributions based on the real data illustrations.

Indexed as

Least square estimationMaximum likelihood estimationMomentsPercentile estimationRenyi entropyStress–strength parameterTopp–Leone model

Identifiers

PMID38625258
PMCPMC9579674

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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.