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Computational Mathematics and Applied Statistics, 2nd Edition
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Computational Mathematics and Applied Statistics, 2nd Edition

A Special Issue of Mathematical and Computational Applications (ISSN 2297-8747).

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1602

Editors

Dr. Sandra Ferreira

E-Mail Website
Guest Editor
1. Mathematics Department, University of Beira Interior, 6201-001 Covilhã, Portugal
2. Centre of Mathematics and Applications, University of Beira Interior, 6201-001 Covilhã, Portugal
Interests: applied statistics; computational mathematical methods; distribution theory; linear models; prediction; statistical inference
Special Issues, Collections and Topics in MDPI journals
Dr. Dário Ferreira

E-Mail Website
Guest Editor
Department of Mathematics and Center of Mathematics, University of Beira Interior, 6200 Covilhã, Portugal
Interests: applied statistics; data science; probability theory; statistical inference; statistical modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The sustained expansion of computational and data-intensive research has further reinforced the importance of rigorous statistical methodology and advanced mathematical modelling in contemporary scientific inquiry. As analytical problems become increasingly complex, the interplay between computational mathematics and applied statistics continues to assume a central role in the development of robust, efficient, and scientifically meaningful solutions.

This second edition builds upon the aims of the first by offering a forum for high-quality contributions in computational statistics, encompassing both methodological innovation and practical applications. The volume reflects the interdisciplinary character of the field and brings together perspectives from statistics, mathematics, computer science, and related areas. Particular attention is given to research with relevance to biometrics, econometrics, data analysis, simulation, scientific computing, and other domains in which quantitative methods are of fundamental importance.

Computational mathematics provides the theoretical and algorithmic framework required to model, analyse, and solve complex problems through advanced numerical and mathematical techniques. Applied statistics, in turn, supplies the inferential principles and analytical tools necessary to interpret data, assess uncertainty, and support sound decision-making across diverse applications. Their close integration has become increasingly significant in the broader development of data science and related disciplines, where mathematical structure, statistical reasoning, and computational implementation must operate in a unified manner.

It is in this context that the present edition is situated. By continuing to promote research at the intersection of these fields, this volume seeks to contribute to ongoing methodological progress and to foster further dialogue across disciplinary boundaries. We hope that it will serve as a valuable reference for researchers, practitioners, and advanced students working in computational mathematics, applied statistics, engineering, computer science, computational intelligence, and data science.

Dr. Sandra Ferreira
Dr. Dário Ferreira
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematical and Computational Applications 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 1600 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

  • multivariate analysis
  • density estimation
  • statistical modelling
  • statistical inference

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Published Papers (3 papers)

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24 pages, 1756 KB  
Article
On the Background Driving Lévy Density Associated with the General Tempered Stable Distribution: Theoretical Properties and Financial Applications
by Aubain Nzokem and Daniel Maposa
Math. Comput. Appl. 2026, 31(4), 159; https://doi.org/10.3390/mca31040159 - 7 Aug 2026
Viewed by 250
Abstract
This paper identifies and characterizes the background driving Lévy process (BDLP) associated with the Generalized Tempered Stable (GTS) distribution, a flexible seven-parameter family of infinitely divisible distributions with applications in physics and quantitative finance. We show that the corresponding BDLP is a finite-variation, [...] Read more.
This paper identifies and characterizes the background driving Lévy process (BDLP) associated with the Generalized Tempered Stable (GTS) distribution, a flexible seven-parameter family of infinitely divisible distributions with applications in physics and quantitative finance. We show that the corresponding BDLP is a finite-variation, infinite-activity Type B Lévy process and derive its explicit background driving characteristic exponent function (BDCEF). The resulting BDLP provides a unified representation that encompasses several important special cases, including the bilateral stable, bilateral Gamma, and Variance Gamma distributions. Building on these results, we develop a simulation framework based on a stationary Ornstein–Uhlenbeck (OU)-type process driven by the GTS BDLP. The mean-reversion speed parameter of the OU process is calibrated using maximum likelihood estimation applied to daily return data from the SPY ETF and Ethereum over the period 2010–2024. The proposed simulation methodology produces realistic daily cumulative return trajectories, and comprehensive numerical error analyses demonstrate the accuracy and efficiency of the resulting discretization scheme. These findings provide both a theoretical extension of Lévy-driven OU models and a practical framework for simulating complex financial return dynamics. Full article
(This article belongs to the Special Issue Computational Mathematics and Applied Statistics, 2nd Edition)
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28 pages, 1329 KB  
Article
Bayesian Prediction of Future Network Latency Times Under Two-Parameter Exponential Current Records
by Ramy A. Aldallal and Metwally R. Alseedy
Math. Comput. Appl. 2026, 31(4), 130; https://doi.org/10.3390/mca31040130 - 10 Jul 2026
Viewed by 272
Abstract
Modeling and predicting extreme observations in network latency data is an important task for monitoring the performance and reliability of communication systems. In this paper, we develop a comprehensive inferential and predictive framework for network latency times using upper and lower current record [...] Read more.
Modeling and predicting extreme observations in network latency data is an important task for monitoring the performance and reliability of communication systems. In this paper, we develop a comprehensive inferential and predictive framework for network latency times using upper and lower current record values from the two-parameter exponential distribution. First, explicit expressions for the probability density functions and cumulative distribution functions of the lower and upper current records are derived. Closed-form expressions for the r-th moments of these records are also obtained. Parameter estimation for the location and scale parameters is then investigated using both maximum likelihood estimation and Bayesian estimation. Because the Bayesian estimators do not admit closed-form solutions, a Markov Chain Monte Carlo approach based on the Metropolis–Hastings algorithm is employed to compute the posterior summaries. To forecast future latency behavior, classical predictive intervals and Bayesian predictive procedures are developed for future upper and lower current records as well as for future record ranges. The Bayesian framework additionally provides Bayesian predictive values and Bayesian predictive intervals that incorporate parameter uncertainty. The performance of the proposed procedures is evaluated through a simulation study. Finally, the methodology is applied to real network latency time data from Saudi Arabia. The results demonstrate that record-based inference combined with Bayesian prediction provides effective tools for modeling and forecasting extreme latency observations in modern communication networks. Full article
(This article belongs to the Special Issue Computational Mathematics and Applied Statistics, 2nd Edition)
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16 pages, 1152 KB  
Article
Evaluating Regularization Estimators Under Severe Multicollinearity: A Simulation and Empirical Study on Housing Prices
by Osman Ufuk Ekiz and Meltem Ekiz
Math. Comput. Appl. 2026, 31(3), 111; https://doi.org/10.3390/mca31030111 - 19 Jun 2026
Viewed by 516
Abstract
Accurate housing price prediction is important for market efficiency and purchasing decisions. However, multicollinearity among independent variables remains a major challenge in linear regression, causing variance inflation and reducing the reliability of the ordinary least squares (OLS) estimator. Although regularization methods such as [...] Read more.
Accurate housing price prediction is important for market efficiency and purchasing decisions. However, multicollinearity among independent variables remains a major challenge in linear regression, causing variance inflation and reducing the reliability of the ordinary least squares (OLS) estimator. Although regularization methods such as ridge regression, least absolute shrinkage and selection operator (LASSO), and elastic net (EN) are widely used, evidence regarding their variance behavior under controlled multicollinearity structures remains limited. This study addresses this gap through simulation experiments conducted under controlled correlation structures with sample sizes ranging from 100 to 2000, 5 to 70 independent variables, and correlation coefficients between 0.50 and 0.99. The findings are further validated using the California Housing Dataset, where mean squared prediction error (MSPE) is computed on the full dataset, while root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) are evaluated on a hold-out test set. Simulation results show that LASSO generally yields the lowest variance estimates under moderate multicollinearity, whereas EN becomes more competitive as multicollinearity and dimensionality increase. In the California Housing application, EN reduces MSPE by approximately 95.5% relative to OLS. These findings provide insight into the behavior of linear regression estimators and offer practical guidance for researchers in selecting appropriate models for housing price modelling. Full article
(This article belongs to the Special Issue Computational Mathematics and Applied Statistics, 2nd Edition)
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