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Regression Diagnostics to Detect Nonrandom Missingness in Linear Regression
Jeffrey S. Simonoff
Vol. 30, No. 2 (May, 1988), pp. 205-214
Published by: Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality
Stable URL: http://www.jstor.org/stable/1270166
Page Count: 10
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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.
Technometrics © 1988 American Statistical Association