Advanced Statistical Applications for Practical Problems in Business

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 172

Special Issue Editor


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Guest Editor
Department of Mathematical Sciences, Bentley University, Waltham, MA, USA
Interests: applied statistics; applied game theory; health analytics; general business analysis; applied mathematics; machine learning; interdisciplinary research

Special Issue Information

Dear Colleagues,

With technology and AI evolving at lightning speed, organizations across all sectors, such as business, healthcare, non-profit, and public management, etc., are facing new opportunities and challenges. This fast-changing landscape calls for advanced statistical and data science solutions that can deliver real-world impact. However, a significant gap remains between cutting-edge theoretical research and practical applications in these fields.

This Special Issue is dedicated to bridging that gap. We invite contributions that showcase innovative uses of advanced statistical methods to solve real-world problems across the broad landscape of business and organizational management. We are especially excited to feature work that not only advances statistical theory but also delivers meaningful, actionable results. Interdisciplinary research at the intersection of statistics, business, technology, and public service is strongly encouraged. We look forward to highlighting ideas that push boundaries, drive innovation, and create real value across industries.

Prof. Dr. Mingfei Li
Guest Editor

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Keywords

  • real-world problem solving
  • advanced statistical methods
  • innovative ideas
  • practical impact
  • management
  • marketing
  • healthcare
  • non-profit
  • public management

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Published Papers (1 paper)

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Research

19 pages, 1509 KB  
Article
A New Two-Component Hybrid Model for Highly Right-Skewed Data: Estimation Algorithm and Application to Rainfall Data from South Tyrol, Italy
by Patrick Osatohanmwen
Mathematics 2025, 13(18), 2987; https://doi.org/10.3390/math13182987 - 16 Sep 2025
Viewed by 103
Abstract
In many real-life processes, data with high positive skewness are very common. Moreover, these data tend to exhibit heterogeneous characteristics in such a manner that using one parametric univariate probability distribution becomes inadequate to model such data. When the heterogeneity of such data [...] Read more.
In many real-life processes, data with high positive skewness are very common. Moreover, these data tend to exhibit heterogeneous characteristics in such a manner that using one parametric univariate probability distribution becomes inadequate to model such data. When the heterogeneity of such data can be appropriately separated into two components—the main innovation component, where the bulk of data is centered, and the tail component which contains some few extreme observations—in such a way, and without a loss in generality, that the data possesses high skewness to the right, the use of hybrid models to model the data becomes very viable. In this paper, a new two-component hybrid model which combines the half-normal distribution for the main innovation of a positive and highly right-skewed data with the generalized Pareto distribution (GPD) for the observations in the data above a certain threshold is proposed. To enhance efficiency in the estimation of the parameters of the hybrid model, an unsupervised iterative algorithm (UIA) is adopted. The hybrid model is applied to model the intensity of rainfall which triggered some debris flow events in the South Tyrol region of Italy. Results from Monte Carlo simulations, as well as from the model’s application to the real data, clearly show how the UIA enhances the estimation of the free parameters of the hybrid model to offer good fits to positive and highly right-skewed data. Application results of the hybrid model are also compared with the results of other two-component hybrid models and graphical threshold selection methodologies in extreme value theory. Full article
(This article belongs to the Special Issue Advanced Statistical Applications for Practical Problems in Business)
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