Simonoff, Jeffrey S. (1988, ASQC and the American Statistical Association) New York University, New York, NY
Missing data is a common problem in regression analysis. The usual estimation strategies require that the data values be missing completely at random (MCAR); if this is not the case, estimates can be severely biased. In this article it is shown that tests can be constructed based on common regression diagnostics to detect non-MCAR behavior. The construction of these tests and their properties when data are missing in one explanatory variable are detailed. Computer simulations indicate good power to detect various non-MCAR processes. Three examples are presented. Extensions to missing data in more than one explanatory variable and to arbitrary regression models are discussed.
Missing data,Outliers,Regression analysis