Manuscript Topics

Statistical learning in the era of big data has seen rapid advancements in methodological innovation and practical applications. As datasets grow in dimensionality and complexity, the 'curse of dimensionality' poses substantial challenges in statistical and mathematical modelling. This issue is pervasive across many theoretical and applied domains, including mathematics, statistics, computer science, and engineering, requiring the development of advanced techniques to tackle the difficulties inherent in high-dimensional data analysis, while ensuring effective solutions and insights in various fields such as economics, healthcare, and environmental sciences. This special issue aims to provide a platform for disseminating cutting-edge research that advances the theoretical foundations of statistical learning while also highlighting innovative applications in real-world big data scenarios. Contributions related to new algorithms, theoretical models, and computational strategies for high-dimensional data analysis are particularly encouraged. The issue also welcomes work focused on bridging the gap between theory and application in various interdisciplinary fields.

Potential topics include, but are not limited to:

• High-dimensional statistical learning methods  
• Feature selection and dimensionality reduction  
• Scalable machine learning algorithms  
• Ensemble methods for big data  
• Bayesian approaches to large-scale data analysis  
• Non-parametric and semi-parametric methods  
• Robust and adaptive methods in big data analysis  
• Data privacy and security in statistical learning  
• Applications of statistical learning in economics, healthcare, and environmental studies  
• Advances in computational efficiency and optimization techniques  
• Deep learning methods for structured and unstructured big data  
• Model validation and evaluation in the context of big data

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