Exploring Statistical Learning: Inference, Optimization, and Real-World Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 47
Special Issue Editor
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
