An introduction to robust data analysis and its applications

Abstract

In the era of burgeoning data science, the application of robust methodologies for data analysis has become paramount across various crucial domains, including healthcare systems, financial predictions, and beyond. Real-world data challenges, characterized by uncertainty, voluminous databases, and high-dimensional samples, necessitate robust techniques that not only handle such complexities efficiently but also yield reliable models. The convergence of robust optimization and data analysis paves the way for the development of sophisticated and innovative solutions to tackle contemporary problems. In the dynamic landscape of data analysis, robust methodologies have emerged as indispensable tools for navigating the uncertainties inherent in real-world datasets. This special issue of the Annals of Operations Research serves as a testament to the growing significance of robust data analysis and its multifaceted applications across diverse domains. The compilation of papers within this issue showcases the breadth and depth of research in the field, covering a spectrum of topics ranging from robust optimization to the application of machine learning techniques in handling uncertainty. These contributions not only advance the theoretical foundations of robust data analysis but also offer practical insights that can inform decision-making processes in various industries.