pith:T44TCBXQ
High-Dimensional Statistics: Reflections on Progress and Open Problems
High-dimensional statistics has evolved to tackle sophisticated problems in complex datasets by building connections across multiple mathematical and computational fields.
arxiv:2605.05076 v2 · 2026-05-06 · math.ST · stat.CO · stat.ME · stat.ML · stat.TH
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\usepackage{pith}
\pithnumber{T44TCBXQD5LFH6RZXQBNEKX5DI}
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Claims
Over the past two decades high-dimensional statistics has evolved to address sophisticated estimation and inference problems in complex datasets, fostering deep connections with optimization, concentration of measure, random matrix theory, information theory, and theoretical computer science.
That the chosen representative advances and highlighted open problems accurately capture the field's most important developments and gaps without systematic omission of major threads.
A survey synthesizing representative advances, common themes, and open problems in high-dimensional statistics while pointing to key entry-point works.
Receipt and verification
| First computed | 2026-05-26T02:04:12.232825Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
9f393106f01f5653fa39bc02d22afd1a1c7233d1b168c1379610c12a7a272721
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/T44TCBXQD5LFH6RZXQBNEKX5DI \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9f393106f01f5653fa39bc02d22afd1a1c7233d1b168c1379610c12a7a272721
Canonical record JSON
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