Pith Number

pith:QDPGSJFV

pith:2026:QDPGSJFVUDAQADFZOQMOUQDWQB

not attested not anchored not stored refs resolved

Modeling Misclassification in Spousal Violence Reporting: Evidence from Bayesian Quantile Regression

James Stamey, Joon Jin Song, Mohammad Arshad Rahman, Yoo-Mi Chin

Bayesian quantile regression for misclassified binary outcomes introduces a latent true response and models false negative and false positive errors separately.

arxiv:2605.15428 v1 · 2026-05-14 · stat.ME

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

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{QDPGSJFVUDAQADFZOQMOUQDWQB}

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 propose a Bayesian quantile regression framework for misclassified binary outcomes that introduces a latent true response and explicitly models false negative and false positive reporting errors. Estimation is performed through a novel Markov chain Monte Carlo (MCMC) algorithm. Simulation studies under varying prior specifications and misclassification rates demonstrate improved performance over models that ignore misclassification.

C2weakest assumption

The framework assumes that misclassification errors (false negatives and false positives) can be parameterized and identified separately from the quantile-specific effects in a way that allows stable MCMC estimation and that the simulation conditions adequately represent real reporting behavior in spousal violence data.

C3one line summary

A Bayesian quantile regression framework for misclassified binary outcomes is proposed and applied to spousal violence data, revealing higher underreporting than overreporting and altered conclusions about associations with employment and wealth.

References

72 extracted · 72 resolved · 0 Pith anchors

[1] 1993 , volume = 1993

[2] Biometrics , year =

[3] Journal of Applied Statistics , volume = 2018

[4] Communications in Statistics - Simulation and Computation , volume = 2020

[5] Rahim Alhamzawi , title =

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:58.153752Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

80de6924b5a0c1000cb97418ea4076804eeb0152a9fc0f36666363a404bf8efa

Aliases

arxiv: 2605.15428 · arxiv_version: 2605.15428v1 · doi: 10.48550/arxiv.2605.15428 · pith_short_12: QDPGSJFVUDAQ · pith_short_16: QDPGSJFVUDAQADFZ · pith_short_8: QDPGSJFV

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/QDPGSJFVUDAQADFZOQMOUQDWQB \
  | 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: 80de6924b5a0c1000cb97418ea4076804eeb0152a9fc0f36666363a404bf8efa

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b6564f7c419e9800c607063f0f86817c80a4338383b3d77cc896cefb12e57026",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-14T21:23:02Z",
    "title_canon_sha256": "07ff1ddc6e67c0873c9221ea493e0032ab444dfceefd97693d55946d90ca4d89"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15428",
    "kind": "arxiv",
    "version": 1
  }
}