In statistical analyses, the term “bootstrapping” refers to methodology that involves randomly sampling a population with replacement. In this module, we’ll discuss some examples of bootstrapping and other stochastic sampling methods I frequently apply in my own analyses.
One of my most frequent uses of bootstrapping and stochastic sampling methods is when doing cross-validation of a statistical model, such as a linear regression model.
When fitting a model to data, an important, but too often overlooked issue is cross-validation; ensuring that the model that you fit to one data set not only fits that data well, but also has good predictive abilities for an equivalent, but separate (ie; independent) data set. However, if all you have is the one data set, cross-validating the model poses a problem.
AIC is a statistic
Example calculating AIC with R