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.

中文翻译：

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缺失协变量的基于经验似然的加权秩回归

本文提出了一种基于经验似然的加权 (ELW) 秩回归方法，用于在某些协变量随机缺失时估计线性回归模型。如果正确指定了缺失概率，则所提出的回归参数的 ELW 估计器在计算上很简单，并且比逆概率加权 (IPW) 估计器具有更好的效率。IPW 和 ELW 估计量的协方差是通过使用诱导平滑方法的变体来估计的，该方法可以绕过误差的密度估计。仿真结果表明ELW方法在有限样本中工作良好。一个真实的数据例子被用来说明所提出的 ELW 方法。