Pith Number

pith:ARFJIWA2

pith:2026:ARFJIWA23OFXTWDFMIPFJWLOT7

not attested not anchored not stored refs pending

On Data Thinning for Model Validation in Small Area Estimation

Paul A. Parker, Sho Kawano, Zehang Richard Li

Data thinning splits area-level survey estimates into independent training and test components to validate small area estimation models without external data.

arxiv:2604.04141 v3 · 2026-04-05 · stat.ME · math.ST · stat.AP · stat.TH

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{ARFJIWA23OFXTWDFMIPFJWLOT7}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

We show that data thinning with these settings provides consistent and stable performance across heterogeneous sampling designs in design-based simulations using American Community Survey microdata.

C2weakest assumption

The thinned training and test components remain independent and that performance metrics on the thinned training component can be meaningfully related to full-data metrics despite targeting a different quantity, with the gap varying by model complexity.

C3one line summary

Data thinning splits area-level observations to enable out-of-sample validation of Fay-Herriot models, with recommendations for thinning parameters that balance bias and variance for stable model comparison.

Formal links

2 machine-checked theorem links

Cited by

2 papers in Pith

Design-Based Cross-Validation for Comparing Small Area Estimators

Receipt and verification

First computed	2026-06-19T16:12:05.755637Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

044a94581adb8b79d865621e54d96e9fcc4cb23f05fdd87398eb97a644aa7cea

Aliases

arxiv: 2604.04141 · arxiv_version: 2604.04141v3 · doi: 10.48550/arxiv.2604.04141 · pith_short_12: ARFJIWA23OFX · pith_short_16: ARFJIWA23OFXTWDF · pith_short_8: ARFJIWA2

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ARFJIWA23OFXTWDFMIPFJWLOT7 \
  | 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: 044a94581adb8b79d865621e54d96e9fcc4cb23f05fdd87398eb97a644aa7cea

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e0cf238c3811ba5e9644e562ce1a376862f06c0f0119b98ec23c29c276132b9f",
    "cross_cats_sorted": [
      "math.ST",
      "stat.AP",
      "stat.TH"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-04-05T14:59:47Z",
    "title_canon_sha256": "07ac83e56ec769d8da82906a8f89c84b5eb0d8c8a97b2517dac9aad67e2b482b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.04141",
    "kind": "arxiv",
    "version": 3
  }
}