Missing data estimators in the general linear model: an evaluation of simulated data as an experimental design |
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Authors: | Alexander Basilevsky Donald Sabourin Derek Hum Andy Anderson |
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Institution: | 1. University of Winnipeg Winnipeg , Canada;2. University of Massachusetts Amherst , MA |
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Abstract: | Previous simulations have reported second order missing data estimators to be superior to the more straightforward first order procedures such as mean value replacement. These simulations however were based on deterministic comparisonsbetween regression criteria even though simulated sampling is a random procedure. In this paper a simulation structured asan experimental design allows statistical testing of the various missing data estimators for the various regression criteria as well as different regression specifications. Our results indicate that although no missing data estimator is globally best many of the computationally simpler first order methods perform as well as the more expensive higher order estimators, contrary to some previous findings. |
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Keywords: | missing data estimators regression simulation exprimental design ANOVA properties of estimators |
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