Journal of the American Statistical Association volume 81, issue 394, P501-509 1986 DOI: 10.1080/01621459.1986.10478296 View full text
Gary A. Simon, Jeffrey S. Simonoff

Abstract: The usual approach to handling missing data in a regression is to assume that the points are missing at random (MAR) and use either a fill-in method to replace the missing points or a method using maximally available pairs in the sample covariance matrix. We derive limits for the values of the least squares estimates of the coefficients (and their associated t statistics) when there are missing observations in one carrier. These limits are derived subject to a constraint on the relationship of the missing dat…

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