Bootstrapping

Bootstrapping can refer to any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Generally, it falls in the broader class of resampling methods.

See also

Statistical testing, Resampling methods, Jackknife

Material

• Bootstrap tutorial

Papers

• Hesterberg, T., Moore, D. S., Monaghan, S., Clipson, A., & Epstein, R. (2005). Bootstrap methods and permutation tests. Introduction to the Practice of Statistics, 5, 1-70.
• Efron, Bradley (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics 7: 1-26.
• Efron, Bradley (1981). Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods. Biometrika 68: 589-599.
• Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods & Research, 21(2), 205-229.

Books

• Efron, Bradley; Tibshirani, Robert J. (1993). An introduction to the bootstrap, New York: Chapman & Hall
• Davison, A. C. and Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge university press.
• Mooney, C Z & Duval, R D (1993). Bootstrapping. A Nonparametric Approach to Statistical Inference. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-095. Newbury Park, CA: Sage.