Advances in High-Dimensional Statistics

Dear Colleagues,

High-dimensional data analysis has emerged as a critical area of research with applications in fields such as machine learning, bioinformatics, and finance. The complexity and scale of high-dimensional datasets pose unique challenges that demand innovative statistical methodologies and computational approaches. This call for paper contributions invites researchers, academics, and practitioners to share their latest findings, methodologies, and applications in the realm of high-dimensional statistics.

We welcome submissions that address a broad range of topics related to advances in high-dimensional statistics, including but not limited to the following:

• Dimensionality reduction techniques;
• Sparse and low-rank modeling;
• High-dimensional inference;
• High-dimensional variable selection;
• Graphical models for high-dimensional data;
• Bayesian methods for high-dimensional problems;
• High-dimensional clustering and classification;
• Nonparametric approaches for high-dimensional data;
• Computational challenges and scalable algorithms;
• Applications of high-dimensional statistics in various domains (e.g., biology, economics, image analysis, etc.).

Dr. Yuwen Gu
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.

• dimensionality reduction techniques
• sparse and low-rank modeling
• high-dimensional inference
• high-dimensional variable selection
• graphical models for high-dimensional data
• Bayesian methods for high-dimensional problems
• high-dimensional clustering and classification
• nonparametric approaches for high-dimensional data
• computational challenges and scalable algorithms
• applications of high-dimensional statistics in various domains (e.g., biology, economics, image analysis, etc.)