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Hello Everyone, I’ve been doing a little reading about Monotone Data
Augmentation using the Monotone Data MCMC Method in SAS Proc MI. I understand
that the procedure involves imputing enough just enough values for certain
variables to produce a monotone pattern of missingness and then using a
regression method for monotone missing data to impute the remaining missing
values. My sense is that there are also other methods of implementing this two
step process. According to the SAS Online Documentation for PROC MI, the Monotone
Data MCMC Method: "… is useful especially when a data set is close
to having a monotone missing pattern. In this case, the method only needs to
impute a few missing values to the data set to have a monotone missing pattern
in the imputed data set. Compared to a full data imputation that imputes all
missing values, the monotone data MCMC method imputes fewer missing values in
each iteration and achieves approximate stationarity in fewer iterations." So it seems clear that the approach works well when the data
approximate a monotone pattern. I have reasons for using the approach that go beyond
the normal consideration of whether the data approximate the monotone pattern
though. So I was wondering if there is any reason to believe that a Monotone
Data Augmentation approach will work poorly when the data deviate substantially
from the pattern. And if so, what would constitute a substantial deviation? Thanks, Paul Paul
J. Miller, Ph.D. |
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