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
Dr. Cecília Castro
Guest Editors

Manuscript Submission Information

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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.