Resampling methods encompass a wide variety of techniques that use empirical data, as opposed to distributional assumptions, to estimate uncertainty about parameter estimates in a statistical model. Bootstrapping and jackknifing are used when parametric measures are difficult to compute or unreliable, but should be used with caution in small samples. Cross-validation is used to prevent over fitting of the data.
Most bootstrapping routines take random, repeated samples with replacement from the original data and can be performed with no assumption of an underlying distribution. Similar to bootstrapping, jackknifing routines systematically omit the ith observation of the sample where i = 1, . . . , n, creating n resampled datasets. In both cases, the statistic of interest is calculated for each resampled dataset, then aggregated to obtain the quantity of interest and its estimated variance. Simple nonparametric bootstrapping should be used with caution for incomplete data, small samples, and samples with substantial numbers of outliers and shouldn’t be used with nonindependent cases. Variants of the bootstrap ameliorate these issues and are commonly used in these cases.
Lastly, cross-validation splits the data sample into k parts. The model is estimated systematically on all subsets with one part excluded in each estimation to be used in the validation of the model. This method can help prevent over fitting a model based on chance occurrences contained within a given dataset.
Bibliography:
- Davison, Anthony C., and D.V. Hinkley. Bootstrap Methods and Their Application. Cambridge: Cambridge University Press, 1997.
- Efron, Bradley, and Robert J.Tibshirani. An Introduction to the Bootstrap. Boca Raton, Fla.: Chapman and Hall/CRC, 1993.
- Good, Phillip I. Resampling Methods: A Practical Guide to Data Analysis. Boston: Birkhauser, 2006.
- Mooney, Christopher Z., and Robert D. Duval. Bootstrapping: A Nonparametric Approach to Statistical Inference. Thousand Oaks, Calif.: Sage, 1993.
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