Dear
imputers
I'm a Ph.D student
and working on missing data, multiple imputation...I'm a little bit
confused and have some questions :
1. When I
estimate the fraction of missing information from a multiple imputation with
Gibbs sampling , I obtained always higher the fraction of missing information than multiple imputation with
stochastic EM. Is this normal? Do you have similar
results?
2. Let's
assume that I have data contains some complete and some incomplete variables,
and I want to estimate the fraction of missing information. I expect that the
fraction of missing information for complete variables should be 0. Is this idea
wrong? Especially, if we impute data with Gibbs, they are not equal to zero..
3. Let's say I want
to estimate the fraction of missing information. I have two options to impute
data: First, I can estimate parameters from Gibbs complete data(i.e. after each
draw of Ymissing, I have a complete data) In this case, I
obtained 0 fraction of missing information for variables that I
have complete data. (Is this improper multiple imputations?). Second, I can
use parameter draws from Gibbs(of course, after convergence) and estimate
the fraction of missing information(I think this os proper imputation??). In
this situation, I don't have 0 fraction of missing information for variables
which are complete. Which method is correct?
4. I have two
different designs(missing by design) for the same data set and I want to compare
these two different designs(i.e. different missing data patterns) using the
fraction of missing information of parameters. Does the fraction of missing
information show only missing information after imputation?Let's say if the
imputation works very well for both designs, then shall we expect the fraction
of missing information be the same amount for both designs? Do you suggest
me any other methods(statistics) to show which designs contain more information
before imputation?
I hope these are not
stupid questions and I can get some reply.
Thanks in advance
for any help.
Feray
Adiguzel
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