The Journal of Advances in Statistical Learning and Data Analysis (JASLDA) publishes original research articles, methodological developments, and comprehensive reviews that contribute to the advancement of theoretical, computational, and applied aspects of statistics, statistical learning, and data analysis.

The scope of the journal covers a wide range of topics, including but not limited to:

• Statistical learning theory, machine learning, and data-driven modeling

• Bayesian inference and modern statistical inference

• Statistical modeling and applications across science and engineering

• Reliability analysis and lifetime data modeling

• Statistical quality control and process monitoring

• Computational statistics, resampling, and simulation-based methods

• Artificial intelligence (AI), business intelligence (BI), and data mining

• Big data analytics and predictive modeling

• Fuzzy Statistics, optimization, and other computationally intensive techniques

JASLDA particularly welcomes papers that introduce innovative statistical or computational methodologies, compare the performance of statistical models using real or simulated data, and connect theory with practice through interdisciplinary applications.

Special issues focusing on emerging topics in statistical learning, data analysis, reliability, and related interdisciplinary areas will be published periodically.

To promote open scientific exchange, JASLDA provides open access to all its publications and employs trusted digital preservation systems to ensure long-term accessibility for libraries and researchers worldwide.