Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
1.9
Journals
Axioms
Special Issues
Recent Stochastic and Statistical Approaches for Modeling Complex Systems and...
Due to scheduled maintenance work on our database systems, there may be short service disruptions on this website between 10:00 and 11:00 CEST on June 14th.
share announcement

Recent Stochastic and Statistical Approaches for Modeling Complex Systems and Dependent Variables

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 443

Special Issue Editor

Dr. Matieyendou Lamboni

E-Mail Website
Guest Editor
1. Department DFR-ST, University of Guyane, 97346 Cayenne, France
2. 228-UMR Espace-Dev, University of Guyane, University of Réunion, IRD, University of Montpellier, 34090 Montpellier, France
Interests: mathematical and statistical modeling; non-independent variables; probability and numerical simulation; gradient
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to advance our understanding regarding the  mathematical and statistical analysis of models or functions at any stage of modeling. It aims to publish articles that present sound and generic approaches, original applications and in-depth comparisons of existing approaches that enable us to better model complex systems, understand and improve models; this includes, but is not limited to, stochastic approximations and analysis,  and statistical estimations or computations of quantities of interest. Issues related to higher-dimensional problems, functional orthogonal basis, uncertainty quantification, and progress involving non-independent variables are of particular interest. Most well-known results  are based on the assumption of separable input variables, and this Special Issue will usefully supplement such results via their adaptations, and highlight developments and functional analysis involving non-independent variables. Finally, this Special Issue aims to collect articles related to engineering, physical, environmental and social sciences, mathematics and statistics to create a forum for the latest research and applications in the aforementioned scientific disciplines. 

Dr. Matieyendou Lamboni
Guest Editor

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. Axioms is an international peer-reviewed open access monthly 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 2400 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

  • mathematical and statistical modeling
  • non-independent variables
  • stochastic approximations and simulations
  • functional basis
  • higher-dimensional problems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 544 KiB  
Article
Estimation of Parameters and Reliability Based on Unified Hybrid Censoring Schemes with an Application to COVID-19 Mortality Datasets
by Mustafa M. Hasaballah, Mahmoud M. Abdelwahab and Khamis A. Al-Karawi
Axioms 2025, 14(6), 460; https://doi.org/10.3390/axioms14060460 - 12 Jun 2025
Abstract
This article presents maximum likelihood and Bayesian estimates for the parameters, reliability function, and hazard function of the Gumbel Type-II distribution using a unified hybrid censored sample. Bayesian estimates are derived under three loss functions: squared error, LINEX, and generalized entropy. The parameters [...] Read more.
This article presents maximum likelihood and Bayesian estimates for the parameters, reliability function, and hazard function of the Gumbel Type-II distribution using a unified hybrid censored sample. Bayesian estimates are derived under three loss functions: squared error, LINEX, and generalized entropy. The parameters are assumed to follow independent gamma prior distributions. Since closed-form solutions are not available, the MCMC approximation method is used to obtain the Bayesian estimates. The highest posterior density credible intervals for the model parameters are computed using importance sampling. Additionally, approximate confidence intervals are constructed based on the normal approximation to the maximum likelihood estimates. To derive asymptotic confidence intervals for the reliability and hazard functions, their variances are estimated using the delta method. A numerical study compares the proposed estimators in terms of their average values and mean squared error using Monte Carlo simulations. Finally, a real dataset is analyzed to illustrate the proposed estimation methods. Full article
(This article belongs to the Special Issue Recent Stochastic and Statistical Approaches for Modeling Complex Systems and Dependent Variables)
►▼ Show Figures

Figure 1

19 pages, 872 KiB  
Article
Variational Bayesian Quantile Regression with Non-Ignorable Missing Response Data
by Juanjuan Zhang, Weixian Wang and Maozai Tian
Axioms 2025, 14(6), 408; https://doi.org/10.3390/axioms14060408 - 27 May 2025
Viewed by 268
Abstract
For non-ignorable missing response variables, the mechanism of whether the response variable is missing can be modeled through logistic regression. In Bayesian computation, the lack of a conjugate prior for the logistic function poses a significant challenge. Introducing a new Pólya-Gamma variable and [...] Read more.
For non-ignorable missing response variables, the mechanism of whether the response variable is missing can be modeled through logistic regression. In Bayesian computation, the lack of a conjugate prior for the logistic function poses a significant challenge. Introducing a new Pólya-Gamma variable and employing lower-bound approximation are two common methods for parameter inference in conjugate Bayesian logistic regression. It can be observed that these two methods yield essentially the same variational posterior in the calculation of the variational Bayesian posterior. This paper applies a popular Bayesian spike-and-slab LASSO prior for variable selection in quantile regression with non-ignorable missing response variables, which demonstrates good performance in both simulations and practical applications. Full article
(This article belongs to the Special Issue Recent Stochastic and Statistical Approaches for Modeling Complex Systems and Dependent Variables)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop