Pith Number

pith:YSJGLHDD

pith:2026:YSJGLHDD456YPTHSVGMCDVC4VU

not attested not anchored not stored refs pending

Predictive Volatility of Machine Learning in Micro-Samples: A Regularised Assessment of Regional Poverty

A. H. Jamaluddin, A. T. R. Dani, N. I. Mahat, S. S. M. Fauzi, V. Ratnasari

Penalized linear models outperform complex ensembles in small-sample provincial poverty analysis and flag ICT as a stable factor.

arxiv:2604.06278 v2 · 2026-04-07 · stat.ME · cs.CY · stat.AP

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\usepackage{pith}
\pithnumber{YSJGLHDD456YPTHSVGMCDVC4VU}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications 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

simple linear shrinkage models (Ridge, Elastic Net, LASSO) achieve the superior out-of-sample prediction, whereas complex ensembles like BART suffer from severe overfitting. ... parametrically regularised linear shrinkage provides a more reliable mathematical foundation for isolating structural development priorities, such as ICT, than either naive OLS or unconstrained machine learning.

C2weakest assumption

That leave-one-out cross-validation on n=34 sufficiently demonstrates general superiority of penalized linear models and that ICT emerges as the key stable proxy without being an artifact of variable selection or collinearity handling in the specific dataset.

C3one line summary

Penalized linear shrinkage models outperform complex ensembles in LOOCV for small-sample high-collinearity Indonesian provincial poverty data, with ICT skills as the stable predictor.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:05:44.405662Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c492659c63e77d87ccf2a99821d45cad0f6a177e7cca09524b466284c916a278

Aliases

arxiv: 2604.06278 · arxiv_version: 2604.06278v2 · doi: 10.48550/arxiv.2604.06278 · pith_short_12: YSJGLHDD456Y · pith_short_16: YSJGLHDD456YPTHS · pith_short_8: YSJGLHDD

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YSJGLHDD456YPTHSVGMCDVC4VU \
  | 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: c492659c63e77d87ccf2a99821d45cad0f6a177e7cca09524b466284c916a278

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "05549324aa29a6140dd9eb3fa968d317a5e726fd1803a815d06c83cd1c5f35f2",
    "cross_cats_sorted": [
      "cs.CY",
      "stat.AP"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-04-07T09:41:12Z",
    "title_canon_sha256": "c7eced1beb2cec8b0684f83ef0137ada048ff79b9cbd343afe8fbe83a9fe28bc"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.06278",
    "kind": "arxiv",
    "version": 2
  }
}