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In the case of a continuous partially observed variable, missing data are imputed using standard MI assuming MAR, and then the imputed values in each imputed datasets are shifted (i.e. add or multiply δ pm, a pattern-mixture model sensitivity parameter, to each of the imputed values) in such a way that they represent the MNAR mechanism.
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This may be due to the fact that the distribution of the MAF in the low coverage pilot dataset was shifted to the left toward the low frequencies compared to the distribution of MAF in the HapMap II dataset.
In the 1D case, the RDIs are shifted upward depending on the distortion strength throughout the dataset (Fig. 19a).
Although this combined normalization may have a slight impact on performance estimates as more test examples are added to the datasets, the performance should be shifted towards better generalizability to the test set.
Things are shifting eastward.
Political dynamics are shifting.
Boundaries, too, are shifting.
Consumer preferences are shifting.
So attitudes are shifting.
Resources are shifting.
We are shifting here.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com