Ben points us to a new book, Flexible Imputation of Missing Data. It’s excellent and I highly recommend it. Definitely worth the $89.95. Van Buuren’s book is great even if you don’t end up using the algorithm described in the book (I actually like their approach but I do think there are some limitations with their particular implementation, which is one reason we’re developing our own package); he supplies lots of intuition, examples, and graphs.
P.S. Stef’s book features an introduction by Don Rubin, which gets me thinking: if Don can find the time to write an introduction to somebody else’s book, he surely should be willing to read and comment on the third edition of his own book, no?
I can vouch for the book as well. I used it as the basis for my Missing Data Imputation lecture in the last PhD class that I taught. It’s great!
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Andrew, excited to learn more about your new imputation package. How does mi() differ from other imputation approaches in R, specifically, aregImpute() from the Hmisc package?
“This graduate-tested book avoids mathematical and technical details as much as possible” (From the publisher’s blurb)
That’s a scary sentence for a book on data-imputation.