I am using random hot-deck imputation on a repeated measures dataset.
I am tempted to use Rubin's rules for pooling the results of multiple imputation, in particular for regression coefficients. Intuitively it seems the average of the coefficient estimates could be used, but I really don't have any insight about pooling the standard errors and I have not seen any literature about this (Little & Rubin's book seems to be silent on the matter, unless I have missed something)
How can I pool regression coefficient estimates and their standard errors when performing random hot-deck imputation ? Pragmatic advice, theoretical justification and/or references to the literature would be most welcome.
Edit: To clarify, by "random hot deck imputation", I mean:
Hot deck imputation involves replacing missing values of one or more variables for a non-respondent (called the recipient) with observed values from a respondent (the donor) that is similar to the non-respondent with respect to characteristics observed by both cases. In some versions, the donor is selected randomly from a set of potential donors, which we call the donor pool; we call these methods random hot deck methods