Bootstrapped Resampling Algorithm

From GM-RKB
Revision as of 12:05, 8 January 2013 by Gmelli (talk | contribs) (Text replace - " (2006)" to " (2006)")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

A Bootstrap Algorithm is a Resampling Algorithm that ...



References

2009

  • http://en.wikipedia.org/wiki/Bootstrapping_(statistics)
    • In Statistics, bootstrapping is a modern, computer-intensive, general purpose approach to Statistical Inference, falling within a 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.
    • The advantage of bootstrapping over analytical methods is its great simplicity - it is straightforward to apply the bootstrap to derive estimates of standard errors and confidence intervals for complex estimators of complex parameters of the distribution, such as percentile points, proportions, odds ratio, and correlation coefficients. The disadvantage of bootstrapping is that while (under some conditions) it is asymptotically consistent, it does not provide general finite-sample guarantees, and has a tendency to be overly optimistic. The apparent simplicity may conceal the fact that important assumptions are being made when undertaking the bootstrap analysis (e.g. independence of samples) where these would be more formally stated in other approaches.

2006

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

2002

  • (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.

1995

1993

1979