Empirical likelihood-based weighted rank regression with missing covariates

This paper proposes an empirical likelihood-based weighted (ELW) rank regression approach for estimating linear regression models when some covariates are missing at random. The proposed ELW estimator of regression parameters is computationally simple and achieves better efficiency than the inverse probability weighted (IPW) estimator if the probability of missingness is correctly specified. The covariances of the IPW and ELW estimators are estimated by using a variant of the induced smoothing method, which can bypass density estimation of the errors. Simulation results show that the ELW method works well in finite samples. A real data example is used to illustrate the proposed ELW method.