Advances in Machine Learning, High-Dimensional Inference, Shrinkage Estimation, and Model Validation

Dear Colleagues,

The landscape of modern statistics is rapidly evolving due to the explosive growth of high-dimensional data and the increasing integration of machine learning (ML) methodologies into applied and theoretical statistical research. These developments have necessitated a rethinking of classical estimation techniques, model assessment strategies, and validation frameworks, particularly under complex, high-dimensional settings. This Special Issue aims to bring together high-quality research contributions that explore theoretical advances, methodological developments, and practical applications in the following intersecting areas:

• Machine learning and statistical learning theory.
• High-dimensional data analysis.
• Shrinkage and regularization techniques (e.g., Lasso, Ridge, and Elastic Net).
• Model selection and validation strategies.
• Applications in genomics, finance, healthcare, and engineering.

We intend to provide a platform for researchers to showcase innovative work that pushes the frontiers of statistical methodology while maintaining a strong link to real-world data and empirical validation.

This Special Issue will provide a focused venue for the dissemination of cutting-edge research at the intersection of statistical theory, machine learning, and high-dimensional inference. It will not only advance scholarly dialogue in these domains but also guide practitioners in applying robust, validated models to complex data challenges.

We look forward to the opportunity to contribute to Stats through this Special Issue.

We look forward to receiving your contributions.

Guest Editors

Prof. Dr. B. M. Golam Kibria
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. Stats is an international peer-reviewed open access quarterly 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 1600 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.

• high-dimensional data
• machine learning (ML) methodologies
• theoretical statistical research