pith:CGWCRL4E
The Threshold Breakdown Point
The threshold breakdown point is the smallest contamination fraction that forces an estimator past a chosen deviation level.
arxiv:2605.04317 v2 · 2026-05-05 · math.ST · stat.TH
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Claims
We define the threshold breakdown point, the smallest contamination fraction needed to induce a prescribed deviation, and the finite sample m-sensitivity, the worst-case deviation that an estimator can incur after m observations are contaminated. We derive these measures for commonly used M-estimators, their standard errors and related test statistics.
The derivations and extensions assume that M-estimators satisfy the regularity conditions needed for explicit breakdown calculations and that the contamination model permits well-defined worst-case deviations; the inferential results further assume standard asymptotic conditions for consistency, normality, and bootstrap validity.
Introduces threshold breakdown point and m-sensitivity as new finite-sample robustness measures for M-estimators and tests, with consistency, asymptotic normality, and multiplier bootstrap inference.
Receipt and verification
| First computed | 2026-05-20T00:03:13.857049Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
11ac28af843b032471b79aa3d95ef9bb570cd7acf0605be07253f1564c8c9e31
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/CGWCRL4EHMBSI4NXTKR5SXXZXN \
| 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())"
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Canonical record JSON
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