High-Dimensional Statistics: Reflections on Progress and Open Problems
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911715132702720 |
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| author | Maleki, Arian Sen, Subhabrata Balakrishnan, Sivaraman Zuber, Verena Gao, Chao Dudeja, Rishabh Thrampoulidis, Christos Zhang, Anru Su, Weijie Klusowski, Jason M. Loh, Po-Ling Shojaie, Ali |
| author_facet | Maleki, Arian Sen, Subhabrata Balakrishnan, Sivaraman Zuber, Verena Gao, Chao Dudeja, Rishabh Thrampoulidis, Christos Zhang, Anru Su, Weijie Klusowski, Jason M. Loh, Po-Ling Shojaie, Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05076 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | High-Dimensional Statistics: Reflections on Progress and Open Problems Maleki, Arian Sen, Subhabrata Balakrishnan, Sivaraman Zuber, Verena Gao, Chao Dudeja, Rishabh Thrampoulidis, Christos Zhang, Anru Su, Weijie Klusowski, Jason M. Loh, Po-Ling Shojaie, Ali Statistics Theory Computation Methodology Machine Learning 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. |
| title | High-Dimensional Statistics: Reflections on Progress and Open Problems |
| topic | Statistics Theory Computation Methodology Machine Learning |
| url | https://arxiv.org/abs/2605.05076 |