I have a data sample (in this case an EEG data sample, but my question refers to any type of data samples of prior unknown distributions).
I would like to do a nonparametric estimate of the expected value for my sample. I did some research, from what I understood I can do this using bootstrap sampling. I found a pdf here giving a formula for bootstrap expected value, hopefully it's correct.
In case it's not, can someone please let me know how to do it once I have generated the samples by bootstrapping?
Another possibility seems to be MCMC, but I would need to know the distribution from what I understood. I could do a kernel density estimation probably, but I think using bootstrapping might be less complex?
I can use python, Matlab or R, in case you do this kind of thing often and have code at hand to share, I'd really appreciate it.
Any other methods/suggestions are more than welcome.