pith:DPICAIE6
Multi-Fidelity Quantile Regression
The high-fidelity quantile equals the low-fidelity quantile evaluated at a covariate-dependent level.
arxiv:2605.10406 v2 · 2026-05-11 · stat.ME · stat.AP · stat.ML
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\usepackage{pith}
\pithnumber{DPICAIE6QIHQLGGVNW5U2KWJLG}
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Record completeness
Claims
The central idea is a local quantile link: at each covariate value, the HF quantile is represented as a low-fidelity (LF) quantile evaluated at a covariate-dependent level. This reformulation reduces the problem to estimating the level function, which can be smoother than the HF quantile itself when the LF and HF conditional distributions have similar shapes.
The LF and HF conditional distributions have similar shapes so that the level function is smoother than the target HF quantile surface.
A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
Formal links
Receipt and verification
| First computed | 2026-06-09T01:05:18.996394Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1bd020209e820f0598d56dbb4d2ac9599f5dbb11c85558fa7eed209ce8268a73
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DPICAIE6QIHQLGGVNW5U2KWJLG \
| 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: 1bd020209e820f0598d56dbb4d2ac9599f5dbb11c85558fa7eed209ce8268a73
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
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"submitted_at": "2026-05-11T11:43:38Z",
"title_canon_sha256": "d328675951532de3f228cb648b4b5d406d6757366580ba7c3e3ab0afd1f8af04"
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