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Activity Number: 614
Type: Invited
Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #307267
Title: Maximum Likelihood Imputation
Author(s): Paul T. von Hippel*+
Companies: The University of Texas
Keywords: missing data ; incomplete data
Abstract:

Maximum likelihood (ML) imputation is a neglected form of multiple imputation (MI) that draws random imputations conditionally on an ML parameter estimate. We contrast ML imputation with the more conventional approach to MI, which we call posterior draw (PD) imputation because it draws imputations conditionally on parameter estimates drawn from the Bayesian posterior distribution of the parameters. Point estimates are more efficient and can be less prone to bias under ML imputation than under PD imputation. Standard errors are harder to estimate under ML imputation, but we present a new standard error formula that is relatively simple and works well provided the fraction of missing information is not too large.


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