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Journal of Jilin University Science Edition

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Weighted Quantile Regression for VaryingCoefficient Modelswith Missing Covariates Based on Empirical Likelihood

YUAN Xiaohui, JU Tingting

School of Basic Science, Changchun University of Technology, Changchun 130012, China

Received:2016-03-02 Online:2017-03-26 Published:2017-03-24
Contact: YUAN Xiaohui E-mail:yuanxh@ccut.edu.cn

Abstract

Abstract: We proposed the inverse probability weighted (IPW) estimation of regression parameters and the empirical likelihoodbased weighted (ELW) estimation for varyingcoefficient quantile regression model with partial missing covariates, and discussed the large sample properties of these two estimations. The ELW estimation is more efficient than the IPW estimation by the asymptotic variance.

Key words: quantile regression, empirical likelihood, missing covariates, inverse probability weighted (IPW) estimation, varying-coefficient

CLC Number:

O212.4

YUAN Xiaohui, JU Tingting. Weighted Quantile Regression for VaryingCoefficient Modelswith Missing Covariates Based on Empirical Likelihood[J].Journal of Jilin University Science Edition, 2017, 55(02): 281-288.

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Weighted Quantile Regression for VaryingCoefficient Modelswith Missing Covariates Based on Empirical Likelihood

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