Using diagnostic measures to detect non-MCAR processes in linear regression models with missing covariates

This paper presents methods to analyze and detect non-MCAR
processes that lead to missing covariate values in linear
regression models. First, the data situation and the problem is
sketched. The next section provides an overview of the methods that
deal with missing covariate values. The idea of using outlier
methods to detect non-MCAR processes is described in section 3.
Section 4 uses these ideas to introduce a graphical method to
visualize the problem. Possible extensions conclude the
presentation.