Semi-parametric rank regression with missing responses

We consider a semi-parametric regression model with responses missing at random and study the rank estimator of the regression coefficient. Consistency and asymptotic normality of the proposed estimator are established. Monte Carlo simulation experiments show that the proposed estimator is more efficient than the least squares estimator whenever the error distribution is heavy tailed or contaminated. When the errors follow a normal distribution, these simulation experiments show that the rank estimator can be more efficient than its least squares counterpart for cases with large proportion of missing responses.