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Empirical likelihood weighted composite quantile regression with partially missing covariates

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  • Jing Sun
  • Yunyan Ma

Abstract

This paper develops a novel weighted composite quantile regression (CQR) method for estimation of a linear model when some covariates are missing at random and the probability for missingness mechanism can be modelled parametrically. By incorporating the unbiased estimating equations of incomplete data into empirical likelihood (EL), we obtain the EL-based weights, and then re-adjust the inverse probability weighted CQR for estimating the vector of regression coefficients. Theoretical results show that the proposed method can achieve semiparametric efficiency if the selection probability function is correctly specified, therefore the EL weighted CQR is more efficient than the inverse probability weighted CQR. Besides, our algorithm is computationally simple and easy to implement. Simulation studies are conducted to examine the finite sample performance of the proposed procedures. Finally, we apply the new method to analyse the US news College data.

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  • Jing Sun & Yunyan Ma, 2017. "Empirical likelihood weighted composite quantile regression with partially missing covariates," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(1), pages 137-150, January.
    • Handle: RePEc:taf:gnstxx:v:29:y:2017:i:1:p:137-150
    DOI: 10.1080/10485252.2016.1272692
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    Cited by:

    1. Jing Sun, 2020. "An improvement on the efficiency of complete-case-analysis with nonignorable missing covariate data," Computational Statistics, Springer, vol. 35(4), pages 1621-1636, December.

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