Pith Number

pith:DOUQCTQE

pith:2026:DOUQCTQEBDATN4TIJNAZDSEOX5

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LOO-PIT predictive model checking

Aki Vehtari, Herman Tesso

Leave-one-out PIT values are dependent in finite samples, so standard uniformity tests for Bayesian model calibration have lower power than expected.

arxiv:2603.02928 v2 · 2026-03-03 · stat.ME · stat.CO

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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

We prove that this dependency is non-negligible in the finite case and depends on model complexity. We propose three testing procedures that can be used for continuous and discrete dependent uniform values... Extensive numerical experiments... demonstrate that the proposed tests achieve competitive performance overall and have much higher power than standard uniformity tests based on the independence assumption.

C2weakest assumption

The dependence structure induced by LOO predictive distributions can be adequately captured by the three proposed testing procedures without introducing new bias or power loss in realistic finite-sample regimes; the abstract provides no detail on how the tests are derived or calibrated.

C3one line summary

New tests for LOO-PIT uniformity account for non-negligible dependence caused by shared data across leave-one-out predictions, achieving higher power than independence-assuming alternatives.

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-18T02:44:30.918889Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1ba9014e0408c136f2684b4191c88ebf7dbf6c8d73914048fd987316bb2ab74b

Aliases

arxiv: 2603.02928 · arxiv_version: 2603.02928v2 · doi: 10.48550/arxiv.2603.02928 · pith_short_12: DOUQCTQEBDAT · pith_short_16: DOUQCTQEBDATN4TI · pith_short_8: DOUQCTQE

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DOUQCTQEBDATN4TIJNAZDSEOX5 \
  | 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: 1ba9014e0408c136f2684b4191c88ebf7dbf6c8d73914048fd987316bb2ab74b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "58779e983ff2a6774c912d94a817648f4c8d52a8a3841da5d175993f22330203",
    "cross_cats_sorted": [
      "stat.CO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-03-03T12:34:52Z",
    "title_canon_sha256": "6a2a8d3dc75e2b67e2012e41c32df77994f26dab3d76d63ed63bfd6d6fda5a7a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.02928",
    "kind": "arxiv",
    "version": 2
  }
}