Submit a Manuscript to the Journal
Journal of Applied Statistics
For a Special Issue on
Applications of Robust Methods in Modern Data Science
Manuscript deadline
Special Issue Editor(s)
Fatma Sevinç Kurnaz (Lead Guest Editor),
Case Western Reserve University and Yildiz Technical University
[email protected]
Abdullah Yalçınkaya,
Department of Statistics, Ankara University
[email protected]
Peter J. Rousseeuw,
Section of Statistics and Data Science, KU Leuven
[email protected]
Peter Filzmoser,
Institute of Statistics and Mathematical Methods in Economics, TU Wien
[email protected]
Olcay Arslan,
Department of Statistics, Ankara University
[email protected]
Yetkin Tuaç,
Department of Statistics, Ankara University
[email protected]
Applications of Robust Methods in Modern Data Science
- Focus on robust statistical methods in modern data analysis under realistic conditions such as outliers, noise, and heterogeneity.
- Emphasis on both methodological developments and practical applications.
- Topics include robust inference in high-dimensional settings, scalable algorithms, and robust approaches for complex data structures (e.g., functional, compositional, and network data).
- Applications in engineering, biomedical sciences, finance, economics, environmental science, and related fields are encouraged.
- Contributions highlighting practical implementation, reproducibility, and real-world case studies are particularly welcome.
- The Special Issue aims to showcase how robust statistical techniques can improve the reliability and interpretability of data-driven analyses.
Submission Instructions
Manuscripts submitted to this Special Issue must follow the Journal of Applied Statistics’ formatting and author guidelines and will undergo a standard double-blind peer-review process with at least two independent reviewers. Authors should indicate during submission that their paper is intended for the Special Issue “Applications of Robust Methods in Modern Data Science” and follow all instructions in the submission system. Submissions must present original, unpublished work not under consideration elsewhere, including both methodological contributions with practical relevance and application-driven studies such as case studies and real-data analyses. While there are no strict page limits beyond the journal’s standard requirements, papers should be concise, and supplementary materials may be included as per journal policy. Accepted papers will be published online on a rolling basis following the journal’s standard production timeline.