| 正文 | In this paper,we present amethod for estimating the conditional distribution
function of the model error. Given the covariates, the conditional mean function is
modeled as a partial linear model, and the conditional distribution function of model
error is modeled as a single-index model. To estimate the single-index parameter,
we propose a semi-parametric global weighted least-squares estimator coupled with
an indicator function of the residuals. We derive a residual-based kernel estimator to
estimate the unknown conditional distribution function. Asymptotic distributions of
the proposed estimators are derived, and the residual-based kernel process constructed
by the estimator of the conditional distribution function is shown to converge to a
Gaussian process. Simulation studies are conducted and a real dataset is analyzed to
demonstrate the performance of the proposed estimators. |
