Advanced Applications with Machine Learning Methods in Applied Statistics

Dear Colleagues, 

This Special Issue of Mathematics, entitled "Advanced Applications with Machine Learning Methods in Applied Statistics", delves into the cutting-edge integration of traditional statistical methods with modern machine learning techniques. The topics covered in this Special Issue range from foundational concepts to practical applications, encompassing a broad spectrum of disciplines.

Key highlights include the following:

1. Comparative Analysis: A thorough comparison of classical statistical techniques and machine learning algorithms in dealing with real-world data challenges.
2. Hybrid Models: An exploration into models that blend the strengths of traditional statistical models with machine learning and deep learning for enhanced uncertainty quantification, predictive accuracy and interpretability.
3. Case Studies: Real-world applications showcasing the efficacy of integrating machine learning methods in sectors such as healthcare, finance, and the environment. 

Throughout this Special Issue, the authors emphasize the importance of understanding the underlying principles of both statistical and machine learning methods, promoting a holistic approach to data analysis.

Dr. Xin Xing
Prof. Dr. Wenxuan Zhong
Guest Editors

Manuscript Submission Information

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• statistical methods
• machine learning
• uncertainty quantification
• predictive accuracy