Hi,
Thank you to everyone who replied to my question (I am wondering whether
anyone can tell me the procedure Bugs uses to impute missing data.
Also, is there a way to first impute the missing data and then run a
Bugs program on it?).
Here are some of the answers I received.
This document has a helpful introduction to missing values in Winbugs:
http://psc.maths.lancs.ac.uk/shortCourses/notes/missingdata/MissingBayesian.beamer.pdf
The following authors have done a lot of research in the area of missing
values analysis:
G. King, P. Allison, J. Graham, D. Rubin, J. Schafer
(fom Sotiris Adamakis)
This powerpoint is also useful for learning about missing values:
home.uchicago.edu/~grynav/bayes/ABSLec18.ppt
(from Kenneth Elgersma)
George Woodworth wrote:
Bugs "imputes" different values for each missing observation in each
MCMCupdate, which is exactly what it does for parameters. The closest
frequentist analog is random imputation. Values are "imputed" from the
conditional distribution of the missing observation given the observed
data and the parameter values in the current update.
You can set the Sample Monitor Tool to monitor the nodes corresponding
to missing observations. For example if you are regressing y on x and,
say, y[3] is missing you can monitor node y[3] to see how Bugs is
"imputing" it. Note that in order to impute an exogenous variable (x in
this case). You would have to specify a distribution (presumably with
unknown parameters) for x as well.
You should NOT impute missing data on your own and run a Bugs program on
it, let Bugs handle the missing values.
***********
Best,
Amalia
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