Advances in Statistical Learning from High-Dimensional Data

The journal Quality and Quantity invites contributions to the special issue “Advances in Statistical Learning from High-Dimensional Data”. The special issue is devoted to one of the most crucial and relevant issues in today's data analysis: extracting meaningful insights from high-dimensional data. The rise of high-dimensional data requires effective analysis and learning methods capable of addressing critical challenges in statistical learning, enabling researchers to harness complex data for better decision-making and more meaningful discoveries. The significance of this special issue lies in the broad applicability of such techniques across various domains, including biomedical sciences, engineering, finance, social sciences, and many others. The ultimate goal is to create a platform for researchers worldwide to exchange ideas, methodologies, and findings, thereby contributing to the advancement of research in this field.

For this special issue, we seek substantive and methodological papers that focus on the following main topics:

A. Supervised classification from high-dimensional data.

B. Unsupervised classification from high-dimensional data.

C. Innovative functional data analysis methods for statistical learning.

D. Causal discoveries from high-dimensional data.

E. Interpretability and explainability in black box models.

F. New ensemble strategies for processing high-dimensional data.

G. Original techniques for learning from data streams.

To submit a paper, please prepare the manuscript according to the Q&Q journal rules and upload it via the online editorial manager system (editorialmanager.com/ququ/default.aspx). In addition, please send the basic information about your submission including the title of the paper, names and affiliations of all authors and abstract to Prof. Fabrizio Maturo (fabrizio.maturo@unimercatorum.it) via email.