Bayesian Multiple Imputation and Maximum Likelihood Methods for Missing Data
نویسنده
چکیده
Bayesian multiple imputation and maximum likelihood provide useful strategy for dealing with dataset including missing values. Imputation methods affect the significance of test results and the quality of estimates. In this paper, the general procedures of multiple imputation and maximum likelihood described which include the normal-based analysis of a multiple imputed dataset. A Monte Carlo simulation is conducted to compare the performances of the methods.
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