The bootstrap method has seen a great diffusion in the last years, I also use it a lot, especially because the reasoning behind is quite intuitive.
But that's one thing I don't understand. Why Efron chose to perform resample with replace instead of simply subsampling by randomly including or excluding single observations?
I think that random subsampling has one very good quality, that is represent ideally the real life situation in which the observations we have in our study are a subset of an hypothetical population. I don't see the advantage of having multiplied observations during resampling. In a real context no observation is similar to another, especially for complex multivariate situations.