Abstract
This paper propose a direct generalization quantile regression estimation method (DGQR estimation) for quantile regression with varying-coefficient models with interval censored data, which is a direct generalization for complete observed data. The consistency and asymptotic normality properties of the estimators are obtained. The proposed method has the advantage that does not require the censoring vectors to be identically distributed. The effectiveness of the method is verified by some simulation studies and a real data example.
Highlights
Varying-coefficient models are among popular models that have been proposed to reduce the curse of dimensionality
We focus on the following varying-coefficient quantile regression model in this article: Data Availability Statement: All relevant data are within the manuscript and its Supporting information files
We summarize our findings below: (1) From Table 1, we can see that the estimation method (DGQR) we proposed in terms of BIAS and mean-squared error (MSE) is superior than the method proposed by Zhou and feng [17], for the quantile regression for varying-coefficient models
Summary
Varying-coefficient models are among popular models that have been proposed to reduce the curse of dimensionality. They were natural extensions of classical parametric models and more popular in data analysis. Cai and Xu [5] studied quantile regression estimation for varying coefficients dynamic models. Yuan and Ju [6] considered a varying-coefficient quantile regression model in which some covariates random missing, and proposed a weighted estimate based on empirical likelihood. Tang and Zhou [7] used inverse probability weighted method in the varyingcoefficient composite quantile regression model with random missing covariates. Sun and Sun [8] proposed optimal inverse probability weighted estimation of regression parameters when selection probabilities were known in the quantile regression model with varying-coefficient
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