I have a very large data (100+ million records)and I am estimating a complex random coefficients model and I can only comfortably use a million or so records. Iam interested in details in time and space so I have sampled on these attributes so that each space-time cell has 10 records. Consequently some cells are overrepresented compared to the population and some are underrepresented. I also have several other variables -all of them categorical and because I can compare the obtained sample with the know frequencies in the population I can evaluate the under and over representation of these types too.
I want to use sampling weights to rebalance the sample as in the population on a number of attributes simultaneously. Can I use a model whether the dependent variable is in sample or not and predictor variables based on type to derive these weights?
Any help much appreciated.