Semiparametric copula-based quantile regression for semicontinuous outcomes with application to healthcare data
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908886368256000 |
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| author | Lyu, Guanjie Belalia, Mohamed Hussein, Abdulkadir |
| author_facet | Lyu, Guanjie Belalia, Mohamed Hussein, Abdulkadir |
| contents | A semiparametric copula-based two-part quantile regression framework is developed for the analysis of semicontinuous outcomes characterized by a point mass at zero and a continuous positive component. The proposed approach models the occurrence and magnitude processes separately and links them through copula-based conditional distributions, allowing for flexible dependence structures and nonlinear covariate effects across quantiles. Large-sample properties of the resulting estimator are established, and extensive simulation studies demonstrate improved finite-sample performance relative to logistic/linear quantile regression, particularly under nonlinear dependence and substantial zero inflation. An application to healthcare data illustrates how the proposed method provides a nuanced characterization of the association between social deprivation and uncompensated and charity care burdens, revealing heterogeneous and nonlinear effects that are not captured by competing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14129 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Semiparametric copula-based quantile regression for semicontinuous outcomes with application to healthcare data Lyu, Guanjie Belalia, Mohamed Hussein, Abdulkadir Methodology 62H05 (Primary) 62G08, 62H20, 62P10 (Secondary) A semiparametric copula-based two-part quantile regression framework is developed for the analysis of semicontinuous outcomes characterized by a point mass at zero and a continuous positive component. The proposed approach models the occurrence and magnitude processes separately and links them through copula-based conditional distributions, allowing for flexible dependence structures and nonlinear covariate effects across quantiles. Large-sample properties of the resulting estimator are established, and extensive simulation studies demonstrate improved finite-sample performance relative to logistic/linear quantile regression, particularly under nonlinear dependence and substantial zero inflation. An application to healthcare data illustrates how the proposed method provides a nuanced characterization of the association between social deprivation and uncompensated and charity care burdens, revealing heterogeneous and nonlinear effects that are not captured by competing approaches. |
| title | Semiparametric copula-based quantile regression for semicontinuous outcomes with application to healthcare data |
| topic | Methodology 62H05 (Primary) 62G08, 62H20, 62P10 (Secondary) |
| url | https://arxiv.org/abs/2603.14129 |