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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.15428 |
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| _version_ | 1866913130890657792 |
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| author | Song, Joon Jin Rahman, Mohammad Arshad Chin, Yoo-Mi Stamey, James |
| author_facet | Song, Joon Jin Rahman, Mohammad Arshad Chin, Yoo-Mi Stamey, James |
| contents | Quantile regression extends regression analysis beyond the conditional mean, providing a richer characterization of covariate effects across the outcome distribution. For sensitive binary outcomes, however, misclassification due to underreporting can substantially bias inference. 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. We apply the method to self-reported spousal violence data, examining associations with employment status and household wealth while adjusting for socio-demographic factors. The results indicate that underreporting exceeds overreporting across quantiles and that accounting for misclassification can change substantive conclusions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15428 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modeling Misclassification in Spousal Violence Reporting: Evidence from Bayesian Quantile Regression Song, Joon Jin Rahman, Mohammad Arshad Chin, Yoo-Mi Stamey, James Methodology Quantile regression extends regression analysis beyond the conditional mean, providing a richer characterization of covariate effects across the outcome distribution. For sensitive binary outcomes, however, misclassification due to underreporting can substantially bias inference. 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. We apply the method to self-reported spousal violence data, examining associations with employment status and household wealth while adjusting for socio-demographic factors. The results indicate that underreporting exceeds overreporting across quantiles and that accounting for misclassification can change substantive conclusions. |
| title | Modeling Misclassification in Spousal Violence Reporting: Evidence from Bayesian Quantile Regression |
| topic | Methodology |
| url | https://arxiv.org/abs/2605.15428 |