Abstract: Regression quantiles can be underpowered or biased when there are missing values in some covariates. We propose a method that produces consistent linear quantile estimation in the presence of missing covariates. The proposed method corrects bias by constructing unbiased estimating equations that simultaneously hold at all the quantile levels. It utilizes all the available data, and produces uniformly consistent estimators. An iterative EM-type algorithm is provided for solving the estimating equations. The finite sample performance of the method is investigated in a simulation study. Finally, the methodology is applied to data from the National Health and Nutrition Examination Survey.

Key words and phrases: Missing data, missing at random, quantile regression, regression quantiles.