I have some survey data on a population described by
age, gender, and weight. It’s quite skewed so I want to reweight it to a known target population (a larger study) using post-stratification.
Problem: some bins are absent in the smaller study (e.g.
age 20-30, weight 120-150lbs, gender M). So when I try to map the population weights from the larger study using inverse proportional fitting, there's a chunk of the population that cannot be mapped. So you end up with an imperfect fit, and even the marginals don't look right (in that example, my overall size of subjects in the age 20-25 bin is off).
In addition there are some bins that have very low N so it's probably not advisable to map the full population weight of that cell onto, say, 1 person!
Is there some way around this? I could imagine some technique where you have a sort of adaptive re-weighting. If the N is too small to split into both
gender M, age 20-25 and, say,
weight 120-150 and
weight 150-180, just group those two cells together into one. But of course there are multiple directions where you could merge cells! Are there some better techniques?