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Distinguishing "Missing at Random" and "Missing Completely at Random"
Daniel F. Heitjan and Srabashi Basu
The American Statistician
Vol. 50, No. 3 (Aug., 1996), pp. 207-213
Stable URL: http://www.jstor.org/stable/2684656
Page Count: 7
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Missing at random (MAR) and missing completely at random (CAR) are ignobility conditions-when they hold, they guarantee that certain kinds of inferences may be made without recourse to complicated missing-data modeling. In this article we review the definitions of MAR, CAR, and their recent generalizations. We apply the definitions in three common incomplete-data examples, demonstrating by simulation the consequences of departures from ignorability. We argue that practitioners who face potentially nonignorable incomplete data must consider both the mode of inference and the nature of the conditioning when deciding which ignorability condition to invoke.
The American Statistician © 1996 American Statistical Association