Bootstrapped Resampling Algorithm

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A Bootstrapped Resampling Algorithm is an out-of-sample evaluation algorithm that is a resampling algorithm (which makes use of bootstrap samples).




  • (Wikipedia, 2013) ⇒ Retrieved:2013-12-4.
    • In statistics, bootstrapping is a method for assigning measures of accuracy to sample estimates. [1] This technique allows estimation of the sampling distribution of almost any statistic using only very simple methods.[2] [3] Generally, it falls in the broader class of resampling methods.

      Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples of the observed dataset (and of equal size to the observed dataset), each of which is obtained by random sampling with replacement from the original dataset.

      It may also be used for constructing hypothesis tests. It is often used as an alternative to inference based on parametric assumptions when those assumptions are in doubt, or where parametric inference is impossible or requires very complicated formulas for the calculation of standard errors.

  1. Efron, B.; Tibshirani, R. (1993). An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC. ISBN 0-412-04231-2.  software
  2. Cite error: Invalid <ref> tag; no text was provided for refs named Varian
  3. Weisstein, Eric W. "Bootstrap Methods." From MathWorld -- A Wolfram Web Resource.



  • (Xia, 2006a) ⇒ Fei Xia. (2006). “Bootstrapping." Course Lecture. LING 572 - Advanced Statistical Methods in Natural Language Processing


  • (Gabor Melli, 2002) ⇒ Gabor Melli. (2002). “PredictionWorks' Data Mining Glossary." PredictionWorks.
    • QUOTE: Bootstrap: A technique used to estimate a model's accuracy. Bootstrap performs [math]b[/math] experiments with a training set that is randomly sampled from the data set. Finally, the technique reports the average and standard deviation of the accuracy achieved on each of the b runs. Bootstrap differs from cross-validation in that test sets across experiments will likely share some rows, while in cross-validation is guaranteed to test each row in the data set once and only once. See also accuracy, resampling techniques and cross-validation.