Authors:Arian Maleki, Subhabrata Sen, Sivaraman Balakrishnan, Verena Zuber, Chao Gao, Rishabh Dudeja, Christos Thrampoulidis, Anru Zhang, Weijie Su, Jason M. Klusowski, Po-Ling Loh, Ali Shojaie
Abstract:Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced the cost and effort for data collection and storage across a broad range of domains, including biology, medicine, astronomy, and the social and environmental sciences. Modern datasets are increasingly complex, often exhibiting rich dependency, heterogeneity, and other features that challenge traditional statistical methods. In response, high-dimensional statistics has evolved to address more sophisticated estimation and inference problems. This evolution has, in turn, fostered deep connections with and contributions to a wide range of research areas, including optimization, concentration of measure, random matrix theory, information theory, and theoretical computer science. Given the rapid pace of recent developments in high-dimensional statistics, our goal is to synthesize representative advances, highlight common themes and open problems, and point to important works that offer entry points into the field.
| Subjects: | Statistics Theory (math.ST); Computation (stat.CO); Methodology (stat.ME); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.05076 [math.ST] |
| (or arXiv:2605.05076v2 [math.ST] for this version) | |
| https://doi.org/10.48550/arXiv.2605.05076 arXiv-issued DOI via DataCite |
From: Arian Maleki [view email]
[v1]
Wed, 6 May 2026 16:11:09 UTC (1,656 KB)
[v2]
Sun, 24 May 2026 22:21:17 UTC (1,656 KB)
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