Is bootstrap a cross-validation?
Both cross validation and bootstrapping are resampling methods. bootstrap resamples with replacement (and usually produces new “surrogate” data sets with the same number of cases as the original data set).
Which is better cross-validation or bootstrap?
For example, bootstrap will likely perform better with small datasets. However it might give overly optimistic results if the training set is wildly different than the test set. 10-times tenfold cross-validation is considered the standard approach for measuring error rates in data mining studies.
How to know if you have enough samples for Bootstrap?
If you are serious about having enough samples, what you should do is to run your bootstrap procedure with, what you hope are, enough samples a number of times and see how much the bootstrap estimates “jump around”. If the repeated estimates does not differ much (where “much” depends on your specific situation) your are most likely fine.
When to use bootstrap in a probability model?
In particular, the bootstrap is only really justified when the moments of the data generating probability model exist. That’s because you are using the empirical distribution function as a straw man for the actual probability model, and assuming they have the same mean, standard deviation, skewness, 99th percentile, etc.
How many bootstrap samples are needed for a leptokurtic distribution?
Any other leptokurtic distribution would require several additional bootstrap iterations compared to a more regular Gaussian density counterpart. In that case, 1000, 100000, or 10000000 bootstrap samples would be insufficient to estimate that which doesn’t exist.
How to calculate bootstrap error in 95% confidence interval?
Bootstrap error in a confidence interval. If using a 95% confidence interval, then look at how variability of the quantiles of the bootstrap distribution near 2.5% and 97.5% by checking the percentiles at (for the 2.5th percentile) 2.5 +/- 2 * 100 * sqrt (0.025 * 0.975 / n).