I am working on a data set which has a 84 variables recorded for a number of individuals. True to "real" data sets, there are many missing variables, NaN values and previous values carried forward (when looking at longitudinal data).
I am trying to find a way to maximise the number of variables present for the largest population, with no values carried forward. That is, how big can I make my population without loosing too many variables.
I have thought about using Expectation Maximisation, although I am not certain at this point how to apply it to this problem. For a lower dimensional problem it could have been possible to create a Venn diagram but in this higher dimensional space, its not really feasible.
I wondered how people have tackled this problem in the past and how you went about solving it. I will start (i think) my making a binary table indicating where values are present/not/carried forward.
Thanks in advance!