Regression Diagnostics to Detect Nonrandom Missingness in Linear Regression

Article

Simonoff, Jeffrey S.   (1988, ASQC and the American Statistical Association)   New York University, New York, NY

Technometrics    Vol. 30    No. 2
QICID: 9351    May 1988    pp. 205-214
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Article Abstract

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.

Keywords

Missing data,Outliers,Regression analysis


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