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Multiple imputation particle filter (MIPF) has been introduced in [122] where particle approximation is utilized to perform multiple imputation.
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Therefore, we use Multiple Imputation (MI) to impute the missing union membership information.
Multiple imputation is used to impute country of birth from the SIPP into the SCF.
This was imputed using a multivariate multiple imputation procedure with 10 imputations.
Multiple imputation using chained equations was used to impute missing covariates; five imputations were generated.
Multiple imputations are needed to properly account for the uncertainty in the imputation process and the resulting set of imputed estimates can be combined using standard multiple imputation combining rules.
Multiple imputation inference procedures are applied to the resulting multiply imputed complete data sets.
We imputed values missing due to discharge or errors using multiple imputation.
To reduce the bias implicit in utilizing only complete cases, multiple imputation procedures were implemented using the SPSS Markov Chain Monte Carlo multiple imputation function to produce five imputed datasets.
Not asked and not answered: multiple imputation for multiple surveys.
Missing stage was handled by multiple imputation approach.
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