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The basic principle is described by Rubin ([2003]): A few imputations of the first part are created (say M), and then for each of these, several imputations of the second part are created (say N).
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Consequently, the MNAR estimate becomes highly dependent on that single imputed dataset as the number of imputations increases, and thus the distribution of the MNAR estimates over 1000 imputations has wider tails than the distribution over fewer imputations.
The model thus predicts the intuitive result that increasing the size of a reference panel (internal or external) leads to fewer imputation errors.
A negative value indicates that the external panel will result in fewer imputation errors on average, and a value of zero indicates that the two panels are expected to produce the same number of incorrectly imputed sites.
Longer haplotypes have a greater accuracy of imputation, but if they are too long they are shared by very few individuals and consequently contribute fewer imputed alleles.
We have interpreted ℙ(C1) as the probability that the internal reference panel is optimal and results in fewer expected imputation errors at a locus than the external panel.
Other important inferential quantities such as null hypothesis significance tests, p values and confidence interval half-widths can suffer from substantial imprecision when just a few number of imputations are used [ 23].
Only a few pedigree-based imputation programs can impute non-genotyped individuals in the pedigree, e.g., AlphaImpute [ 4], FImpute [ 16], FindHap [ 17], and PedImpute [ 18].
To deal with missing data for anxiety during pregnancy (in the other variables there were only few missings), multiple imputation was performed according to the Predicted Mean Matching method.
Otherwise, when country of birth is unavailable for only a few periods, they do imputations based on the information in the available years.
Impute: impute: Imputation for microarray data.
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