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Mathematics
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Exploring Statistical Learning: Inference, Optimization, and Real-World Applications
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Exploring Statistical Learning: Inference, Optimization, and Real-World Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 3120

Special Issue Editor

Dr. Carmela Iorio

E-Mail Website
Guest Editor
Department of Economics and Statistics, University of Naples Federico II, 80138 Napoli, NA, Italy
Interests: machine learning; data mining; linear and non-linear regression; supervised and unsupervised learning; time series analysis; statistics for finance

Special Issue Information

Dear Colleagues,

"Exploring Statistical Learning: Inference, Optimization, and Real-World Applications" presents a comprehensive investigation into the multifaceted domain of statistical learning. This Special Issue encompasses a wide spectrum of topics, from foundational principles of inference and optimization to their practical manifestations in real-world contexts. The Issue elucidates the intricacies of statistical learning algorithms and their applications across diverse domains such as finance, healthcare, and marketing through a combination of theoretical insights and empirical studies. This collection bridges the gap between theory and practice, equipping readers with a deeper understanding of statistical learning methodologies and their transformative potential in addressing contemporary data analysis and decision-making challenges.

Dr. Carmela Iorio
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. Mathematics 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 2600 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

  • statistical learning
  • inference
  • optimization
  • real-world applications
  • data analysis
  • predictive modeling
  • machine learning
  • supervised learning
  • unsupervised learning
  • deep learning
  • computational statistics
  • model evaluation
  • decision-making
  • data-driven solutions

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

Published Papers (5 papers)

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Research

25 pages, 1891 KiB  
Article
Classification Improvement with Integration of Radial Basis Function and Multilayer Perceptron Network Architectures
by László Kovács
Mathematics 2025, 13(9), 1471; https://doi.org/10.3390/math13091471 - 30 Apr 2025
Abstract
The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integration of these tools is an interesting but understudied problem domain. This [...] Read more.
The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integration of these tools is an interesting but understudied problem domain. This paper presents a novel initialization method based on a distance-weighted homogeneity measure to construct a radial basis function network with fast convergence. The proposed radial basis function network is utilized in the development of an integrated RBF-MLP architecture. The proposed neural network model was tested in various classification tasks and the test results show superiority of the proposed architecture. The RBF-MLP model achieved nearly 40 percent better accuracy in the tests than the baseline MLP or RBF neural network architectures. Full article
(This article belongs to the Special Issue Exploring Statistical Learning: Inference, Optimization, and Real-World Applications)
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28 pages, 10436 KiB  
Article
ParDP: A Parallel Density Peaks-Based Clustering Algorithm
by Libero Nigro and Franco Cicirelli
Mathematics 2025, 13(8), 1285; https://doi.org/10.3390/math13081285 - 14 Apr 2025
Viewed by 191
Abstract
This paper proposes ParDP, an algorithm and concrete tool for unsupervised clustering, which belongs to the class of density peaks-based clustering methods. Such methods rely on the observation that cluster representative points (centroids) are points of higher local density surrounded by points of [...] Read more.
This paper proposes ParDP, an algorithm and concrete tool for unsupervised clustering, which belongs to the class of density peaks-based clustering methods. Such methods rely on the observation that cluster representative points (centroids) are points of higher local density surrounded by points of lesser density. Candidate centroids, though, are to be far from each other. A key factor of ParDP is adopting a k-Nearest Neighbors (kNN) technique for estimating the density of points. Complete clustering depends on densities and distances among points. ParDP uses principal component analysis to cope with high-dimensional data points. The current implementation relies on Java parallel streams and the built-in lock-free fork/join mechanism, enabling the exploitation of the computing power of commodity multi/many-core machines. This paper demonstrates ParDP’s clustering capabilities by applying it to several benchmark and real-world datasets. ParDP’s operation can either be directed to observe the number of clusters in a dataset or to finalize clustering with an assigned number of clusters. Different internal and external measures can be used to assess the accuracy of a resultant clustering solution. Full article
(This article belongs to the Special Issue Exploring Statistical Learning: Inference, Optimization, and Real-World Applications)
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22 pages, 7778 KiB  
Article
A New Approach to Estimate the Parameters of the Joint Distribution of the Wind Speed and the Wind Direction, Modelled with the Angular–Linear Model
by Samuel Martínez-Gutiérrez, Alejandro Merino, Luis A. Sarabia, Daniel Sarabia and Ruben Ruiz-Gonzalez
Mathematics 2025, 13(8), 1238; https://doi.org/10.3390/math13081238 - 9 Apr 2025
Viewed by 180
Abstract
In order to assess the potential and suitability of a location to deploy a wind farm, it is essential to have a model of the joint probability density function of the wind speed and direction, fV,Θ(v,θ [...] Read more.
In order to assess the potential and suitability of a location to deploy a wind farm, it is essential to have a model of the joint probability density function of the wind speed and direction, fV,Θ(v,θ). The angular–linear model is widely used to obtain the analytical expression of the joint density from the parametric estimation of the probability density functions of wind speed, fV(v), and wind direction, fΘ(θ). In previous studies, the parameters of the marginal distributions were obtained by fitting the wind measurements to the cumulative distribution function (CDF) using the least squares method and then calculating the probability density function (PDF). In this study, we propose to directly fit the probability density function and then calculate the cumulative distribution function. It is shown that it has both computational and goodness-of-fit advantages. In addition, previous studies have been expanded, analysing the effect of the number of intervals on which wind speed and direction ranges are divided. The new parameter fitting method is evaluated and compared with the original proposal in terms of goodness of fit, using the coefficient of determination R2 as an estimator both in the probability density function (R2pdf) and in the cumulative distribution function (R2cdf). The computational times required to estimate the parameters using both methods will also be compared. The new approach is faster, and the goodness of the fitting is satisfactory for both estimators: it produces a better R2pdf, without significantly affecting the R2cdf, in contrast to the initial one where the R2pdf is smaller. Full article
(This article belongs to the Special Issue Exploring Statistical Learning: Inference, Optimization, and Real-World Applications)
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14 pages, 1082 KiB  
Article
Interpreting Temporal Shifts in Global Annual Data Using Local Surrogate Models
by Shou Nakano and Yang Liu
Mathematics 2025, 13(4), 626; https://doi.org/10.3390/math13040626 - 14 Feb 2025
Cited by 1 | Viewed by 525
Abstract
This paper focuses on explaining changes over time in globally sourced annual temporal data with the specific objective of identifying features in black-box models that contribute to these temporal shifts. Leveraging local explanations, a part of explainable machine learning/XAI, can yield explanations behind [...] Read more.
This paper focuses on explaining changes over time in globally sourced annual temporal data with the specific objective of identifying features in black-box models that contribute to these temporal shifts. Leveraging local explanations, a part of explainable machine learning/XAI, can yield explanations behind a country’s growth or downfall after making economic or social decisions. We employ a Local Interpretable Model-Agnostic Explanation (LIME) to shed light on national happiness indices, economic freedom, and population metrics, spanning variable time frames. Acknowledging the presence of missing values, we employ three imputation approaches to generate robust multivariate temporal datasets apt for LIME’s input requirements. Our methodology’s efficacy is substantiated through a series of empirical evaluations involving multiple datasets. These evaluations include comparative analyses against random feature selection, correlation with real-world events as explained using LIME, and validation through Individual Conditional Expectation (ICE) plots, a state-of-the-art technique proficient in feature importance detection. Full article
(This article belongs to the Special Issue Exploring Statistical Learning: Inference, Optimization, and Real-World Applications)
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18 pages, 3527 KiB  
Article
Identification of Patterns in CO2 Emissions among 208 Countries: K-Means Clustering Combined with PCA and Non-Linear t-SNE Visualization
by Ana Lorena Jiménez-Preciado, Salvador Cruz-Aké and Francisco Venegas-Martínez
Mathematics 2024, 12(16), 2591; https://doi.org/10.3390/math12162591 - 22 Aug 2024
Cited by 1 | Viewed by 1487
Abstract
This paper identifies patterns in total and per capita CO2 emissions among 208 countries considering different emission sources, such as cement, flaring, gas, oil, and coal. This research uses linear and non-linear dimensional reduction techniques, combining K-means clustering with principal component analysis [...] Read more.
This paper identifies patterns in total and per capita CO2 emissions among 208 countries considering different emission sources, such as cement, flaring, gas, oil, and coal. This research uses linear and non-linear dimensional reduction techniques, combining K-means clustering with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), which allows the identification of distinct emission profiles among nations. This approach allows effective clustering of heterogeneous countries despite the highly dimensional nature of emissions data. The optimal number of clusters is determined using Calinski–Harabasz and Davies–Bouldin scores, of five and six clusters for total and per capita CO2 emissions, respectively. The findings reveal that for total emissions, t-SNE brings together the world’s largest economies and emitters, i.e., China, USA, India, and Russia, into a single cluster, while PCA provides clusters with a single country for China, USA, and Russia. Regarding per capita emissions, PCA generates a cluster with only one country, Qatar, due to its significant flaring emissions, as byproduct of the oil industry, and its low population. This study concludes that international collaboration and coherent global policies are crucial for effectively addressing CO2 emissions and developing targeted climate change mitigation strategies. Full article
(This article belongs to the Special Issue Exploring Statistical Learning: Inference, Optimization, and Real-World Applications)
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