𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗥𝗼𝗯𝘂𝘀𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 𝗶𝗻 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲

𝗖𝗔𝗟𝗟 𝗙𝗢𝗥 𝗣𝗔𝗣𝗘𝗥𝗦

𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗥𝗼𝗯𝘂𝘀𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 𝗶𝗻 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲

𝗝𝗼𝘂𝗿𝗻𝗮𝗹:
Journal of Applied Statistics

𝗣𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗿:
Taylor & Francis Group

𝗠𝗮𝗻𝘂𝘀𝗰𝗿𝗶𝗽𝘁 𝗗𝗲𝗮𝗱𝗹𝗶𝗻𝗲:
31 October 2026

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲

Modern data science increasingly deals with complex datasets characterized by outliers, noise, heterogeneity, and high dimensionality. Robust statistical methods have emerged as essential tools for ensuring reliable inference, accurate prediction, and meaningful interpretation under such challenging conditions.

This Special Issue seeks high-quality contributions that advance robust statistical methodologies and demonstrate their practical applications across a variety of real-world domains. The issue will showcase how robust approaches can strengthen data-driven decision-making while improving the reliability and interpretability of analytical outcomes.

𝗧𝗼𝗽𝗶𝗰𝘀 𝗼𝗳 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁

Submissions may address, but are not limited to:

• Robust inference in high-dimensional data settings
• Scalable algorithms for robust statistical analysis
• Robust methods for functional, compositional, and network data
• Outlier detection and treatment techniques
• Robust machine learning and predictive modeling
• Applications in engineering, biomedical sciences, finance, economics, and environmental science
• Reproducible research and practical implementation of robust methodologies
• Real-world case studies and application-driven analyses

𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 𝗠𝗮𝘁𝘁𝗲𝗿𝘀

As organizations increasingly rely on data-driven insights, the ability to produce reliable results from imperfect and complex data has become more important than ever. Robust statistical techniques offer powerful solutions for handling uncertainty, anomalies, and data quality issues, making them indispensable in modern analytics and decision-making environments.

This Special Issue aims to bridge methodological innovation and practical implementation by highlighting robust approaches that can improve the quality, transparency, and trustworthiness of modern data science applications.

𝗚𝘂𝗲𝘀𝘁 𝗘𝗱𝗶𝘁𝗼𝗿𝘀

Fatma Sevinç Kurnaz, Abdullah Yalçınkaya, Peter J. Rousseeuw, Peter Filzmoser, Olcay Arslan, Yetkin Tuaç

𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀

• Manuscripts must follow the Journal of Applied Statistics author and formatting guidelines.
• All submissions will undergo a standard double-blind peer-review process with at least two independent reviewers.
• Authors should indicate that their manuscript is intended for the Special Issue “Applications of Robust Methods in Modern Data Science.”
• Papers must present original, unpublished work that is not under consideration elsewhere.
• Both methodological contributions and application-oriented studies are welcome.
• Accepted papers will be published online on a rolling basis following the journal’s standard production schedule.

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗝𝗼𝘂𝗿𝗻𝗮𝗹

Journal of Applied Statistics publishes high-quality research on statistical methodology and its practical applications across science, engineering, medicine, economics, business, environmental studies, and related disciplines. The journal promotes innovative statistical approaches that address real-world analytical challenges and support evidence-based decision-making.

𝗣𝗼𝘀𝘁𝗲𝗱 𝗼𝗻 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝗦𝗲𝘁𝘂 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰𝘀 — 𝗣𝗿𝗲𝗺𝗶𝗲𝗿 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗳𝗼𝗿 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 & 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻

Visit official website of the publisher