pith:MJMGSNKT
A Grid-Rate Condition for Valid Uniform Inference
For functions in a Donsker class, the grid-growth condition L_n = ω(r_n^{1/4}) suffices for valid uniform inference on twice continuously differentiable functions.
arxiv:2605.12284 v2 · 2026-05-12 · econ.EM
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Claims
for functions within a Donsker class, the simple grid-growth condition L_n=ω(r_n^{1/4}) is sufficient for valid inference for twice continuously differentiable functions estimable at the r_n^{1/2} rate. This condition ensures that the approximation error is asymptotically negligible relative to the stochastic variation of the empirical process.
The approximation error from discretizing the domain onto L_n^d grid points is asymptotically negligible relative to the stochastic variation of the empirical process; this relies on the target function being twice continuously differentiable and belonging to a Donsker class.
L_n = ω(r_n^{1/4}) ensures valid uniform inference for twice differentiable Donsker-class functions estimated at the r_n^{1/2} rate by making grid approximation error negligible relative to stochastic variation.
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| First computed | 2026-06-08T01:04:07.338926Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6258693553857c8ac38d718b454af14d035410128257706f2b9b5dd9cba75b0b
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/MJMGSNKTQV6IVQ4NOGFUKSXRJU \
| 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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