Recent Advances in Statistical Modeling and Simulations with Applications, 2nd Edition

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1204

Special Issue Editor


E-Mail Website
Guest Editor
Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, 83200 Karlovassi, Greece
Interests: big data; machine learning; neural networks; image analysis; medical imaging; Bayesian statistics; applied statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Every application is the result of a simulation process, which involves the use of advanced statistical techniques and is one of the most important aspects of data processing and analysis. The main purpose of statistical modeling is to simulate natural complex phenomena—both for analysis and to predict future processes. Therefore, the objective of any simulation is to identify the optimal or satisfactory solution to a problem through the operation of a real system. Often, failure to meet the above objectives is due to the complexity of the problem subjected to dig data processes.

Developing, supporting, and using simulation models requires statistical models in the form of statistical distributions. The important role of statistics is evident in simulations of reality, which are more realistic when the various variables and parameters are stochastic in nature. Statistical techniques are widely used to evaluate and predict variables, especially those used for big data, examples of which include medical images or general images, such as machine learning and neural networks.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Big data;
  • Machine learning;
  • Neural networks;
  • Image analysis;
  • Medical imaging;
  • Bayesian statistics;
  • Applied statistics;
  • Medical statistics;
  • Ecology;
  • Environmental sciences.

Dr. Stelios Zimeras
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

  • big data
  • machine learning
  • neural networks
  • image analysis
  • medical imaging
  • Bayesian statistics
  • applied statistics

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

23 pages, 469 KiB  
Article
Variational Bayesian Estimation of Quantile Nonlinear Dynamic Latent Variable Models with Possible Nonignorable Missingness
by Mulati Tuerde and Ahmadjan Muhammadhaji
Axioms 2024, 13(12), 849; https://doi.org/10.3390/axioms13120849 - 3 Dec 2024
Viewed by 710
Abstract
Our study presents an innovative variational Bayesian parameter estimation method for the Quantile Nonlinear Dynamic Latent Variable Model (QNDLVM), particularly when dealing with missing data and nonparametric priors. This method addresses the computational inefficiencies associated with the traditional Markov chain Monte Carlo (MCMC) [...] Read more.
Our study presents an innovative variational Bayesian parameter estimation method for the Quantile Nonlinear Dynamic Latent Variable Model (QNDLVM), particularly when dealing with missing data and nonparametric priors. This method addresses the computational inefficiencies associated with the traditional Markov chain Monte Carlo (MCMC) approach, which struggles with large datasets and high-dimensional parameters due to its prolonged computation times, slow convergence, and substantial memory consumption. By harnessing the deterministic variational Bayesian framework, we convert the complex parameter estimation into a more manageable deterministic optimization problem. This is achieved by leveraging the hierarchical structure of the QNDLVM and the principle of efficiently optimizing approximate posterior distributions within the variational Bayesian framework. We further optimize the evidence lower bound using the coordinate ascent algorithm. To specify propensity scores for missing data manifestations and covariates, we adopt logistic and probit models, respectively, with conditionally conjugate mean field variational Bayes for logistic models. Additionally, we utilize Bayesian local influence to analyze the Ecological Momentary Assessment (EMA) dataset. Our results highlight the variational Bayesian approach’s notable accuracy and its ability to significantly alleviate computational demands, as demonstrated through simulation studies and practical applications. Full article
Show Figures

Figure 1

Back to TopTop