Applied Statistics in Real-World Problems

Dear Colleagues,

The convergence of statistical methodologies and real-world challenges within different sciences offers a rich tapestry of innovation. From unraveling the complexities to decoding information, statistical techniques serve as the linchpin connecting theory to application. This Special Issue is a canvas for articles that delve into the dynamic landscape where statistics intersects with diverse scientific domains applied to a real-life world.

Contributions are welcome from a spectrum of disciplines, including causality, medicine, economics, epidemiology, sociology, biochemistry, biophysics, neurology, psychology, etc. Furthermore, we invite researchers from other branches, fields, or sub-disciplines to share their insights into the latest statistical methodologies.

An inherent criterion for each submission is accessibility. Craft your articles with clarity in mind, catering to an audience that may not be intimately familiar with the specific terminology of your field. Consider employing aids such as tables featuring key concepts or a glossary to enhance reader understanding.

In the spirit of practicality, we encourage the incorporation of real datasets to illustrate the statistical methods applied. To promote transparency and facilitate scientific rigor, authors are kindly asked to share the codes and datasets used in their analyses, utilizing platforms such as Python, R, or others.

We anticipate a collection of articles that not only showcases statistical innovations but also inspires a cross-disciplinary dialogue, enriching our collective understanding of the intricate relationship between statistical methodologies and real-world challenges.

This Special Issue is seeking submissions in applied data science with potential applications in, but not limited to, the following:

(a) Artificial intelligence;

(b) Bayesian methods;

(c) Big data, dimensionality high, and large-scale data analysis;

(d) Causality;

(e) Deep and statistical learning;

(f) Machine learning;

(g) Statistical learning.

Dr. Raydonal Ospina
Prof. Dr. Victor Leiva
Guest Editors

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.

• artificial intelligence
• big data, big data analytics, and big data science
• bioinformatics, health informatics, and bio-computing
• causality
• data analytics, data mining, and expert systems
• decision support systems and knowledge discovery in databases
• deep learning, machine learning, and statistical learning
• differential privacy
• digital transformation and digitization
• monitoring/recognizing/forecasting of emotions and sentiment analysis
• multivariate analysis
• optimization algorithms
• predictive models and analytics using artificial intelligence quality control
• statistical analysis/modeling and its diagnostics
• survey sampling