On Jointly Estimating Parameters and Missing Data by Maximizing the Complete-Data Likelihood.
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WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER
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One approach to handling incomplete data occasionally encountered in the literature is to treat the missing data as parameters and to maximize the complete data likelihood over missing data and parameters. This paper points out that although this approach can be useful in particular problems, it is not a generally reliable approach to the analysis of incomplete data. In particular, it does not share the optimal properties of maximum likelihood estimation, except under the trivial asymptotics in which the proportion of missing data goes to zero as the sample size increases. Author
- Statistics and Probability