"Bootstrap validation"/"resampling cross-validation" is new to me, but was discussed by the answer to [this question][1]. I gather it involves 2 types of data: the real data and simulated data, where a given set of simulated data is generated from the real data by resampling-with-replacement until the simulated data has the same size as the real data. I can think of two approaches to using such data types: (1) fit the model once, evaluate it many times on many simulated data sets; (2) fit the model many times using each of many simulated data sets, each time evaluate it against the real data. Which (if either) is best?


  [1]: https://stats.stackexchange.com/questions/11602