Bootstrapped Resampling Algorithm

AKA: Bootstrap Resampling.
Context:
- It can be applied by a Parameter Estimation System (to solve a Parameter Estimation Task.
- It produces a Bootstrap Distribution.
- It can be used to improve the Accuracy of an Supervised Learning Algorithm by Resampling with replacement.
Example(s):
- a Bagging Algorithm (Breiman, 1996)
- a Bootstrapped Semi-Supervised Learning Algorithm.
- a Jackknife Algorithm.
- …
Counter-Example(s):
- a Cross-Validation Algorithm.
See: Semi-Supervised Learning Algorithm, Cross-Validation, Random Sampling With Replacement, Bootstrapped Algorithm.

References

(Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Resampling_%28statistics%29#Bootstrap Retrieved:2014-10-4.
- Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio, correlation coefficient or regression coefficient. It may also be used for constructing hypothesis tests. It is often used as a robust 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.

(Wikipedia, 2013) ⇒ http://en.wikipedia.org/wiki/Bootstrapping_(statistics) 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] 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.

↑ Efron, B.; Tibshirani, R. (1993). An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC. ISBN 0-412-04231-2. software
↑ Weisstein, Eric W. “Bootstrap Methods." From MathWorld -- A Wolfram Web Resource. http://mathworld.wolfram.com/BootstrapMethods.html

(Sammut & Webb, 2011) ⇒ Claude Sammut (editor), and Geoffrey I. Webb (editor). (2011). “Bootstrap Sampling.” In: (Sammut & Webb, 2011) p.137

(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]\displaystyle{ 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.

(Kohavi, 1995) ⇒ Ron Kohavi. (1995). “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995).
- QUOTE: We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap.

(Efron, 1979) ⇒ Bradley Efron. (1979). “Bootstrap Methods: Another Look at the Jackknife.” In: The Annals of Statistics, 7(1). http://www.jstor.org/stable/2958830
- QUOTE: … given a random sample [math]\displaystyle{ \mathbf{X} = (X_1, X_2, ..., X_n) }[/math] from an unknown probability distribution [math]\displaystyle{ F }[/math], estimate the sampling distribution of some prespecified random variable [math]\displaystyle{ R(\mathbf{X}, F) }[/math], on the basis of the observed data [math]\displaystyle{ \mathbf{x} }[/math]. (Standard jackknife theory gives an approximate mean and variance in the case [math]\displaystyle{ R(\mathbf{X},F)=\theta(\hat{F})-\theta(F), \theta }[/math] some parameter of interest.) A general method, called the “bootstrap," is introduced, and shown to work satisfactorily on a variety of estimation problems. The jackknife is shown to be a linear approximation method for the bootstrap. The exposition proceeds by a series of examples: variance of the sample median, error rates in a linear discriminant analysis, ratio estimation, estimating regression parameters, etc.