The R package retimes has a function for fitting an ex-Gaussian distribution to a set of observations. The method involves taking multiple bootstrapped samples of the observations, and fitting the ex-Gaussian to each sample using maximum likelihood estimation. This generates a distribution of parameter values for each of the three parameters of the ex-Gaussian. These distributions are then smoothed, and the mode of each is taken as the best estimate of that parameter of the ex-Gaussian distribution.
My questions are:
- In what circumstances is this method better than a single maximum likelihood estimate on the observed data?
- Is there a reference for this technique that would explain the motivation and justify its use? There are several references listed in the documentation for the retimes package, but none of them appear to deal with the bootstrapping technique.