Bayesian Quantile Regression for Misclassified Binary Data with an Application to Spousal Violence Reporting
误分类二元数据的贝叶斯分位数回归及配偶暴力报告应用AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要核心是误分类二元响应的分位数特定协变量效应及其异质性，因此归入分位数与分布效应方向。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
误分类二元数据的贝叶斯分位数回归及配偶暴力报告应用
- 研究问题
- 社会敏感行为的二元调查回答存在漏报和误报时，如何估计潜在真实响应的分位数特定协变量效应，并揭示条件均值模型可能掩盖的异质性？
- 方法与思路
- 作者在贝叶斯二元分位数回归中同时引入假阴性和假阳性概率，采用部分折叠 Gibbs 采样，并计算协变量效应与边际似然。
- 材料陈述的贡献
- 摘要称考虑误分类可改善模拟中的分位数推断；配偶暴力应用显示漏报超过误报，且模型比较和相关因素判断随误分类处理而变化。
- 阅读关注点
- 应核对潜在真实二元响应的分位数如何定义、误分类概率的识别来源、先验敏感性，以及采样算法和边际似然计算是否稳定。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明误分类识别条件、调查数据来源及具体估计值，应用结论的稳健性仍需全文核对。
原文摘要
Survey responses on socially undesirable behaviors, such as self-reported spousal violence, are often subject to underreporting due to social stigma, fear of retaliation, and other reporting pressures. When such data are analyzed using standard econometric models that focus on conditional means, such as probit and logit models, the resulting estimates are likely to be biased and can obscure heterogeneity in covariate effects. To address these challenges, we propose a Bayesian binary quantile regression framework that accounts for misclassification and provides quantile-specific effects for the latent true response. The framework incorporates false-negative and false-positive probabilities to capture reporting errors and employs a novel partially collapsed Gibbs sampler for estimation. We also discuss the computation of covariate effects and marginal likelihood for Bayesian model comparison. Simulation studies under various settings (prior effective sample size, misclassification rates, and prior distribution) show that accounting for misclassification improves inference across quantiles relative to models that ignore reporting errors. We apply the framework to women's self-reported spousal violence and find that underreporting of spousal violence exceeds overreporting across quantiles, while model comparisons using marginal likelihood generally favor the quantile model with misclassification and yield different conclusions about the determinants of reported violence.