🤖 AI Summary
This study addresses the challenge of jointly modeling the probability of zero outcomes and the heterogeneous, nonlinear effects in the positive component of semi-continuous data—characterized by a substantial mass at zero and a continuous positive part—along with their complex dependence structure. To this end, we propose the first copula-based semiparametric two-part quantile regression framework. The approach separately models the occurrence of zeros and the magnitude of positive values via quantile regression and flexibly captures their nonlinear dependence across quantiles using a copula function. Theoretical analysis establishes large-sample asymptotic properties, and simulations demonstrate superior performance over existing methods under high zero-inflation and nonlinear scenarios. An empirical application to healthcare data reveals heterogeneous and nonlinear effects of social deprivation on uncompensated and charitable care burdens.
📝 Abstract
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.