Mathematical Modelling and Applied Statistics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1088

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1. Applied Digital Transformation Laboratory (ADiT-Lab), Polytechnic Institute of Viana do Castelo, Viana do Castelo, Portugal
2. Center for Research & Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal
Interests: optimal control; nonlinear optimization; mathematical modelling; biomathematics
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Dear Colleagues,

The Special Issue is dedicated to advancing research at the intersection of mathematical theory and practical applications across various scientific and engineering disciplines. It serves as a platform for the dissemination of innovative methodologies, theoretical advancements, and real-world applications of mathematical modeling and statistical analysis. We welcome high-quality original research, review articles, and case studies that contribute to the development, validation, and application of mathematical and statistical techniques in diverse fields such as physics, engineering, biology, economics, finance, social sciences, and data science.

Dr. Helena Sofia Rodrigues
Guest Editor

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Keywords

  • mathematical modeling
  • applied statistics
  • interdisciplinary applications

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Published Papers (1 paper)

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Research

24 pages, 7349 KB  
Article
Return Level Prediction with a New Mixture Extreme Value Model
by Emrah Altun, Hana N. Alqifari and Kadir Söyler
Mathematics 2025, 13(17), 2705; https://doi.org/10.3390/math13172705 - 22 Aug 2025
Viewed by 374
Abstract
The generalized Pareto distribution is frequently used for modeling extreme values above an appropriate threshold level. Since the process of determining the appropriate threshold value is difficult, a mixture of extreme value models rises to prominence. In this study, mixture extreme value models [...] Read more.
The generalized Pareto distribution is frequently used for modeling extreme values above an appropriate threshold level. Since the process of determining the appropriate threshold value is difficult, a mixture of extreme value models rises to prominence. In this study, mixture extreme value models based on exponentiated Pareto distribution are proposed. The Weibull, gamma, and log-normal models are used as bulk densities. The parameter estimates of the proposed models are obtained using the maximum likelihood approach. Two different approaches based on maximization of the log-likelihood and Kolmogorov–Smirnov p-value are used to determine the appropriate threshold value. The effectiveness of these methods is compared using simulation studies. The proposed models are compared with other mixture models through an application study on earthquake data. The GammaEP web application is developed to ensure the reproducibility of the results and the usability of the proposed model. Full article
(This article belongs to the Special Issue Mathematical Modelling and Applied Statistics)
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