缺失数据下基于经验似然的加权复合分位数回归推断

Abstract

Abstract:

We proposed a weighted composite quantile regression method based on empirical likelihood in linear model with some covariates missing at random, and proved the large sample properties of the proposed method under the missing at random mechanism. The results show that the proposed method is computationally simple and the estimation efficiency of the regression parameters is higher than that of the inverse probability weighted method.

Key words: linear model, missing at random, empirical likelihood, composite quantile regression, inverse probability weighting

CLC Number:

O212.4

YUAN Xiaohui, ZHAO Xuedong. Estimation of Weighted Composite Quantile Regression withMissing Covariates Based on Empirical Likelihood[J].Journal of Jilin University Science Edition, 2016, 54(05): 1008-1016.

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Estimation of Weighted Composite Quantile Regression withMissing Covariates Based on Empirical Likelihood

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