# Difference between revisions of "Bootstrap resampling"

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\section*{Bootstrap Resampling} | \section*{Bootstrap Resampling} | ||

− | Bootstrap resampling is a statistical technique to measure the error in a given statistic that has been computed from a sample population. It is a simple yet powerful methord that relies heavily on computational power. | + | Bootstrap resampling is a statistical technique to measure the error in a given statistic that has been computed from a sample population. It is a simple yet powerful methord that relies heavily on computational power. The basic premise is that instead of using a theoretical or mathematical model for the parent distribution from which our observed samples were drawn from, we can use the distribution of the observed samples as an approximation for the parent distribution. |

+ | |||

+ | \subsection*{The Algorithm} | ||

+ | |||

</latex> | </latex> |

## Revision as of 12:32, 13 December 2012

### Prerequisites

### Short topical video

### Reference Material

- Efron, Bradley; Tibshirani, Robert J. (1993). An introduction to the bootstrap
- Bootstrapping (wikipedia)

## Bootstrap Resampling

Bootstrap resampling is a statistical technique to measure the error in a given statistic that has been computed from a sample population. It is a simple yet powerful methord that relies heavily on computational power. The basic premise is that instead of using a theoretical or mathematical model for the parent distribution from which our observed samples were drawn from, we can use the distribution of the observed samples as an approximation for the parent distribution.