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Mathematics
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Clustered Data Modeling and Statistical Meta-Analysis
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Clustered Data Modeling and Statistical Meta-Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 758

Special Issue Editors

Dr. Yehenew Kifle

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD 21250, USA
Interests: multivariate analysis; frailty models; clustered data modeling; statistical meta-analysis
Dr. Roel Braekers

E-Mail Website
Guest Editor
Data Science Institute, University of Hasselt, Diepenbeek 3590, Limburg, Belgium
Interests: multivariate analysis, clustered survival models, copula models, frailty models, nonparametric statistics, modeling associations and dependencies

Special Issue Information

Dear Colleagues,

You are kindly invited to contribute to this Special Issue on “Clustered Data Modeling and Statistical Meta-analysis” with an original research paper or a comprehensive review. Clustered data modeling constitutes a cornerstone in research, with its profound implications spanning various fields such as biostatistics, social sciences, economics, healthcare, and beyond. In parallel, statistical meta-analysis assumes a vital role in the synthesis and analysis of data derived from diverse sources and studies. This Special Issue is primarily centered around new theoretical proposals, practical applications, and/or computational aspects related to both clustered data modeling and statistical meta-analysis.

The Special Issue welcomes original manuscripts or comprehensive reviews on a broad variety of topics in Clustered Data Modeling and Statistical Meta-analysis, including, but not limited to, hierarchical linear models, generalized linear mixed models, longitudinal data analysis, multilevel modeling, Bayesian approaches to clustered data, clustered survival data analysis, meta-analysis methodologies and techniques, meta-regression and subgroup analysis, publication bias and small-study effects, Bayesian meta-analysis, network meta-analysis, multivariate meta-analysis, model diagnostics, as well as software tools and packages in clustered data modeling and statistical meta-analysis.

Dr. Yehenew Kifle
Dr. Roel Braekers
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • clustered data
  • hierarchical data
  • association modeling
  • survival analysis
  • statistical meta-analysis
  • big data integration
  • research synthesis
  • model diagnostics

Published Papers (1 paper)

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Research

18 pages, 397 KiB  
Article
Shrinkage Testimator for the Common Mean of Several Univariate Normal Populations
by Peter M. Mphekgwana, Yehenew G. Kifle and Chioneso S. Marange
Mathematics 2024, 12(7), 1095; https://doi.org/10.3390/math12071095 - 5 Apr 2024
Viewed by 454
Abstract
The challenge of combining two unbiased estimators is a common occurrence in applied statistics, with significant implications across diverse fields such as manufacturing quality control, medical research, and the social sciences. Despite the widespread relevance of estimating the common population mean μ, [...] Read more.
The challenge of combining two unbiased estimators is a common occurrence in applied statistics, with significant implications across diverse fields such as manufacturing quality control, medical research, and the social sciences. Despite the widespread relevance of estimating the common population mean μ, this task is not without its challenges. A particularly intricate issue arises when the variations within populations are unknown or possibly unequal. Conventional approaches, like the two-sample t-test, fall short in addressing this problem as they assume equal variances among the two populations. When there exists prior information regarding population variances (σi2,i=1,2), with the consideration that σ12 and σ22 might be equal, a hypothesis test can be conducted: H0:σ12=σ22 versus H1:σ12≠σ22. The initial sample is utilized to test H0, and if we fail to reject H0, we gain confidence in incorporating our prior knowledge (after testing) to estimate the common mean μ. However, if H0 is rejected, indicating unequal population variances, the prior knowledge is discarded. In such cases, a second sample is obtained to compensate for the loss of prior knowledge. The estimation of the common mean μ is then carried out using either the Graybill–Deal estimator (GDE) or the maximum likelihood estimator (MLE). A noteworthy discovery is that the proposed preliminary testimators, denoted as μ^PT1 and μ^PT2, exhibit superior performance compared to the widely used unbiased estimators (GDE and MLE). Full article
(This article belongs to the Special Issue Clustered Data Modeling and Statistical Meta-Analysis)
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