Goodness-of-fit tests for linear regression models with missing response data

The authors show how to test the goodness-of-fit of a linear regression model when there are missing data in the response variable. Their statistics are based on the L-2 distance between nonparametric estimators of the regression function and a root n-consistent estimator of the same function under the parametric model. They obtain the limit distribution of the statistics and check the validity of their bootstrap version. Finally, a simulation study allows them to examine the behaviour of their tests, whether the samples are complete or not.