Pith Number

pith:3ZPWUNLE

pith:2026:3ZPWUNLESFAQQE67SOUWHMYKSL

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Design-Based Cross-Validation for Comparing Small Area Estimators

Qianyu Dong, Zehang Richard Li

A decomposition of cross-validated squared error separates identifiable bias from bounded unidentifiable parts, enabling reliable comparisons of small area estimators under complex survey designs.

arxiv:2604.23464 v3 · 2026-04-25 · stat.ME · stat.AP

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\pithnumber{3ZPWUNLESFAQQE67SOUWHMYKSL}

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Our theoretical results and simulation studies show that conventional approaches, such as leave-one-area-out cross-validation, can yield misleading model rankings, whereas the proposed approach offers more robust and interpretable model comparison with uncertainty quantification.

C2weakest assumption

The assumption that the proposed decomposition of the cross-validated squared error can effectively bound the unidentifiable components in a manner that enables reliable model comparisons under complex survey designs.

C3one line summary

A cross-validation framework for small area estimation decomposes error to separate measurable bias from bounded unknowns, showing that leave-one-area-out methods can produce misleading model rankings while the new approach provides more robust comparisons and uncertainty measures.

Receipt and verification

First computed	2026-06-11T00:08:12.594224Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

de5f6a356491410813df93a963b30a92ed56a10e4f1b6b8945afe36f42888ce1

Aliases

arxiv: 2604.23464 · arxiv_version: 2604.23464v3 · doi: 10.48550/arxiv.2604.23464 · pith_short_12: 3ZPWUNLESFAQ · pith_short_16: 3ZPWUNLESFAQQE67 · pith_short_8: 3ZPWUNLE

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3ZPWUNLESFAQQE67SOUWHMYKSL \
  | 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: de5f6a356491410813df93a963b30a92ed56a10e4f1b6b8945afe36f42888ce1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "fdf6eba71ddb942900e25c53ebcaf64d3312adc15b50af31899375426e2be721",
    "cross_cats_sorted": [
      "stat.AP"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-04-25T23:02:42Z",
    "title_canon_sha256": "8716100d5fdd84a965b1379481fec8d35af3ef72eeb5227ab70e1a3bb555344d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.23464",
    "kind": "arxiv",
    "version": 3
  }
}