Two-Stage Estimation of Partially Linear Varying Coefficient Quantile Regression Model with Missing Data

In this paper, the statistical inference of the partially linear varying coefficient quantile regression model is studied under random missing responses. A two-stage estimation procedure is developed to estimate the parametric and nonparametric components involved in the model. Furthermore, the asym...

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Bibliographic Details
Main Authors: Shuanghua Luo, Yuxin Yan, Cheng-yi Zhang
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/12/4/578
Description
Summary:In this paper, the statistical inference of the partially linear varying coefficient quantile regression model is studied under random missing responses. A two-stage estimation procedure is developed to estimate the parametric and nonparametric components involved in the model. Furthermore, the asymptotic properties of the estimators obtained are established under some mild regularity conditions. In addition, the empirical log-likelihood ratio statistic based on imputation is proposed, and it is proven that this statistic obeys the standard Chi-square distribution; thus, the empirical likelihood confidence interval of the parameter component of the model is constructed. Finally, simulation results show that the proposed estimation method is feasible and effective.
ISSN:2227-7390