Applications of Statistical and Mathematical Models

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 779

Special Issue Editors


E-Mail Website
Guest Editor
Department of Mathematics, North Carolina A&T State University, Greensboro, NC 27411, USA
Interests: nonparametric statistics; geostatistics; neural networks and machine learning; data science

E-Mail Website
Guest Editor
Department of Statistics, Keimyung University, Daegu 42601, Republic of Korea
Interests: complex network system; artificial intelligence; neural network; long range dependence; nonparametric function testing and estimation; economic crisis modeling; financial trading system; Bayesian theory; spatial statistics

Special Issue Information

Dear Colleagues,

Focus: Our primary focus in this Special Issue is to delve deep into the realm of advanced applications for statistical and mathematical models when tackling the intricacies of real-life situations. Within this exploration, our mission is to uncover the intricate interconnections between modeling and various other domains, encompassing economics, biology, engineering, and the social sciences.

Scope: This Special Issue includes a wide range of topics, including, but not limited to, the following:

  • Statistical and mathematical modeling both computational and experimental in nature;
  • Machine learning, and data science;
  • Complex systems such as ecological networks, financial markets, and social networks;
  • Policy and decision making;
  • Risk assessment from healthcare to environmental management.

Purpose: The purpose of this Special Issue is to expand the boundaries of knowledge and application in the realm of statistical and mathematical models, providing a platform for researchers to exchange innovative methodologies and discoveries.

Relationship to Existing Literature: In relation to the existing literature, this Special Issue is a natural extension, introducing novel applications of statistical and mathematical models across diverse disciplines. It complements prior research by offering fresh insights, methodologies, and real-world case studies that vividly illustrate the effectiveness of these models in unraveling complex challenges and enriching the existing body of knowledge.

Dr. Tamer M Elbayoumi
Prof. Dr. Tae Yoon Kim
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. 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 modeling
  • statistical modeling
  • complex systems
  • machine learning
  • data science
  • decision making
  • risk assessment

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 polices can be found here.

Published Papers (1 paper)

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

Research

15 pages, 1485 KiB  
Article
Analysis of Fat Big Data Using Factor Models and Penalization Techniques: A Monte Carlo Simulation and Application
by Faridoon Khan and Olayan Albalawi
Axioms 2024, 13(7), 418; https://doi.org/10.3390/axioms13070418 - 21 Jun 2024
Viewed by 423
Abstract
This article assesses the predictive accuracy of factor models utilizing Partial·Least·Squares (PLS) and Principal·Component·Analysis (PCA) in comparison to autometrics and penalization techniques. The simulation exercise examines three types of scenarios by introducing the issues of multicollinearity, heteroscedasticity, and autocorrelation. The number of predictors [...] Read more.
This article assesses the predictive accuracy of factor models utilizing Partial·Least·Squares (PLS) and Principal·Component·Analysis (PCA) in comparison to autometrics and penalization techniques. The simulation exercise examines three types of scenarios by introducing the issues of multicollinearity, heteroscedasticity, and autocorrelation. The number of predictors and sample size are adjusted to observe the effects. The accuracy of the models is evaluated by calculating the Root·Mean·Square·Error (RMSE) and the Mean·Absolute·Error (MAE). In the presence of severe multicollinearity, the factor approach utilizing (PLS demonstrates exceptional performance in comparison. Autometrics achieves the lowest RMSE and MAE values across all levels of heteroscedasticity. Autometrics provides better forecasts with low and moderate autocorrelation. However, Elastic·Smoothly·Clipped·Absolute·Deviation (E-SCAD) forecasts well with severe autocorrelation. In addition to the simulation, we employ a popular Pakistani macroeconomic dataset for empirical research. The dataset contains 79 monthly variables from January 2013 to December 2020. The competing approaches perform differently compared to the simulation datasets, although “The PLS factor approach outperforms its competing approaches in forecasting, with lower RMSE and MAE”. It is more probable that the actual dataset exhibits a high degree of multicollinearity. Full article
(This article belongs to the Special Issue Applications of Statistical and Mathematical Models)
Show Figures

Figure 1

Back to TopTop