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arXiv · 2605.15428

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

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

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.

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Joon Jin Song, Mohammad Arshad Rahman, Yoo-Mi Chin, James Stamey. 2026-05-14. Modeling Misclassification in Spousal Violence Reporting: Evidence from Bayesian Quantile Regression. https://arxiv.org/abs/2605.15428

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