Statistical Models and Their Applications

Dear Colleagues,

In today's information-rich landscape—characterized by a diverse range of digital records, high-throughput and complex experimentation, passive data collection, and real-time decision systems—the demand for novel and robust statistical modeling methodologies has never been greater. Traditional statistical modeling frameworks are being stretched to their limits by the complexity and scale of modern data, necessitating the development of novel and improved modeling frameworks that can deliver efficient, effective, and scientifically rigorous inferences. The elimination of the so-called replication crisis through effective statistical inference methodologies is a major concern. Therefore, this Special Issue seeks to bring together cutting-edge research that advances the theory of statistical modeling and explores innovative applications across a wide range of scientific and technological domains.

The Special Issue invites original research and review articles that advance the theory and practice of statistical modeling and its applications. We seek contributions that address the urgent need for innovative statistical models tailored to the evolving landscape of scientific inquiry, as well as new applications of established modeling frameworks in emerging domains. We aim to showcase cutting-edge research that achieves the following:

• Develops novel statistical models tailored to modern data structures and scientific requirements;
• Extends traditional modeling frameworks to emerging application domains;
• Addresses methodological gaps in current statistical practice, and shows ways of integrating statistical modeling with domain expertise;
• Provides robust theoretical foundations for contemporary statistical inference, computational tools, and statistical software;
• Demonstrates practical impact through real-world applications and the communication and visualization of statistical results;
• Addresses the current replication crisis in science through effective statistical inference methodologies.

Topics of Interests:

We invite submissions covering, but not limited to, the following core statistical modeling approaches:

• Causal Graphical Models: Directed acyclic graphs (DAGs), also known as Bayesian networks, chain graph models, causal discovery algorithms, and general causal inference;
• Time Series and Temporal Models: Dynamic models, state space frameworks, functional time series, and non-stationary processes;
• Probabilistic Models: Bayesian models, probabilistic reasoning, and approaches of uncertainty quantification;
• Survival Analysis Models: Competing risks and modeling of longitudinal data and time-to-event survival data;
• Prediction and Clustering and Classification Methods/Models: State-of-the-Art techniques for supervised and unsupervised learning, with emphasis on interpretability and scalability, machine learning models and their statistical interpretations, ensemble methods, deep learning models and their statistical foundations, and interpretable AI;
• Foundations of Statistical Inference: Replication crisis in science, frequentist hypothesis testing and associated p-value problems, Bayesian hypothesis testing, and decision theoretic approaches;
• Reinventing Traditional Models: Novel applications or extensions of classic statistical models to contemporary problems in science, including those in machine learning, big data analytics, and real-time decision-making.

Dr. Priyantha Wijayatunga
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 250 words) can be sent to the Editorial Office for assessment.

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.

• causal inference
• probabilistic
• bayesian
• prediction
• classification and regression
• statistical learning
• temporal
• survival