Data-Driven Statistical Methods
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 766
Special Issue Editor
Special Issue Information
Dear Colleagues,
We are pleased to invite you to contribute to our upcoming Special Issue on "Data-Driven Statistical Methods". This Special Issue aims to highlight recent advancements in statistical methods for analyzing large and complex datasets. With the ever-increasing amount of data being generated in various fields, including healthcare, social media, finance, and environmental science, there is a growing need for novel statistical techniques to extract meaningful insights from the data. This Special Issue invites original research papers, reviews, and case studies on topics related to data-driven statistical methods, including but not limited to machine learning, deep learning, Bayesian inference, high-dimensional data analysis, and causal inference. The goal is to bring together researchers from different disciplines and provide a platform for discussing the latest trends and challenges in this rapidly evolving field.
The explosion of data in many fields has led to statistical methods becoming an essential tool for extracting insights and making informed decisions. We believe that this Special Issue will provide a unique opportunity to showcase your research and contribute to the advancement of this exciting field. We welcome original research articles, reviews, and case studies that address the latest developments and challenges in data-driven statistical methods. We look forward to your contributions and hope this Special Issue will stimulate fruitful discussions and collaborations among researchers from different backgrounds.
Dr. Ernesto Lee
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
- machine learning
- statistical methods
- Bayesian inference
- high-dimensional data analysis
- data-driven
