I have MLE curve parameter estimates for 3 populations (2 per population), and am looking for a clever way to compare them. At the moment, I am non-parametrically bootstrapping my datasets in order to create n datasets, which I then compare using a standard MANOVA procedure. I have a number of issues with this, one of them being that the variances of my bootstrapped parameters are heteroskedastic and non normally distributed.
Ideally, I was thinking that I could compare the bootstrapped confidence intervals around my parameters, i.e. show that the bootstrapped confidence intervals around each of my popuation parameters "overlap".
I was looking at bivariate comparisons, but many of these rely on the fact that it is a MV gaussian distribution, which according to multivariate shapiro tests, I do not have.
To wrap things up, my questions come down to this:
- What would people see as the most sound procedure?
- I had a look through this post Comparing points in a bivariate space, however, am curious about the second answer and if anybody has any suggestions as to where I could find information on non normal mulitvariate analysis.
Thanks for your help.