Abstract
The Akaike information criterion is a frequently used diganostic to select one
or a few regression models from a set of candidate models. An additional
and often neglected model selection problem is introduced if part of the
sample is missing and the expectation-maximization algorithm is used to
obtain maximum likelihood estimates, namely, the choice
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