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Mathematics
Special Issues
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 "Mathematics and Computer Science".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1123

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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Published Papers (1 paper)

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Research

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
Viewed by 716
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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