Quantile Regression Based on the Weighted Approach with Dependent Truncated Data

This paper discusses the estimation of parameters in the quantile regression model for dependent truncated data. To account for the dependence between the survival time and the truncated time, the Archimedean copula model is used to construct the association. The parameters of the Archimedean copula model are estimated using certain existing approaches. An inference procedure based on a weighted approach is proposed, where the weights are set according to the variables of interest in the quantile regression model. The finite sample performance of the proposed approach is examined through simulations, and the method is applied to analyze two real datasets: the transfusion-related AIDS dataset and the retirement community center dataset.

Automated summary

This paper discusses parameter estimation in the quantile regression model for dependent truncated data. The Archimedean copula model is used to account for the dependence between survival time and truncated time. The parameters of the Archimedean copula model are estimated using existing approaches, and a weighted inference procedure is proposed based on the variables of interest in the quantile regression model. The proposed approach is evaluated through simulations and applied to analyze real datasets.