pith:RXSRGA4B
Empirical Bernstein Confidence Intervals for Kernel Smoothers: A Safe and Sharp Way to Exhaust Assumed Smoothness
Empirical Bernstein calibration produces kernel smoother intervals that attain nominal coverage and minimax widths by treating bias on the original scale.
arxiv:2605.03781 v3 · 2026-05-05 · math.ST · stat.TH
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Claims
Uniformly over functions with S-th order local smoothness, both one-sided and two-sided intervals attain the nominal coverage level up to a remainder of order n^{-2S/(2S+1)}, or an exponential remainder in bounded or sub-Gaussian settings. Their widths shrink at the minimax rate n^{-S/(2S+1)}.
The target function lies in a local Taylor-remainder class with exactly S-th order smoothness, and the kernel and bandwidth satisfy the conditions needed for the bias-aware radius construction and empirical Bernstein bounds to apply.
Empirical Bernstein confidence intervals for kernel smoothers attain nominal coverage up to a remainder of order n to the minus 2S over 2S+1 while achieving minimax optimal widths under S-th order local smoothness.
Receipt and verification
| First computed | 2026-05-28T01:04:41.545679Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8de51303814394e22d8bca21c9a10b00942b3d18728882470253be03b239c464
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/RXSRGA4BIOKOELMLZIQ4TIILAC \
| 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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