TLDR Bootstrapping resulted in a crazy scatterplot. Totally clueless here.
I have a dataset (not going to say much about it, it is a bit confidential), running a Markov chain-based algorithm on the data. 458 data points in total.
Wanted to see the volatility of the metric I'm interested in (it is a ratio), decided to bootstrap my 458 data points and compare the results to the original rates, thereby seeing whether there is a relationship between rates and over/underestimations. (I hoped for something linear, so I could create a correctional multiplier). So I didn't simply do bootstrapping until convergence, just a large enough number of samples to see whether there is a relationship which I obviously miss if I stick to my original data and the ratio given one algorithm run.
Problem is, rates calculated from the bootstrapped samples exhibit a very peculiar behavior, not seen in other applications on similar datasets. It seems there are 3 distinct strands of points, one intermixed with a loose cloud of others. I wanted to explore the relationship b/w over/under and rate, but I'm clueless here.
(Besides delving into the data more,) do you happen to have any ideas what to go for?