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.

翻译：本文针对具有零点聚集和连续正分量特征半连续型结果的分析，提出了一种基于半参数Copula的两部分分位数回归框架。该方法分别对发生过程与强度过程进行建模，并通过基于Copula的条件分布将其关联起来，从而允许灵活的相关结构以及协变量在不同分位数上的非线性效应。我们建立了所得估计量的大样本性质，并通过大量模拟研究证明，相较于逻辑/线性分位数回归方法，所提方法在有限样本下（特别是在非线性相关和严重零膨胀情形下）具有更优的性能。通过对医疗数据的实际应用，本文展示了所提方法如何精细刻画社会剥夺程度与无偿及慈善医疗负担之间的关联，揭示了传统竞争方法未能捕捉的异质性与非线性效应。