Rééchantillonnage

Rééchantillonnage is a statistical technique used to estimate population parameters or to assess the uncertainty of an estimate by repeatedly drawing samples from a dataset. This method is particularly useful when the underlying distribution of the data is unknown or complex, or when the sample size is small. The core idea is to use the observed data as a surrogate for the true population.

The process typically involves randomly drawing samples with replacement from the original dataset. Each resample, called a bootstrap sample, is the same size as the original dataset. From each bootstrap sample, a statistic of interest, such as the mean, median, or standard deviation, is calculated. By repeating this process many times (often thousands of times), a distribution of these statistics is generated. This distribution of bootstrap statistics can then be used to estimate the standard error of the statistic, construct confidence intervals, or perform hypothesis tests.

Rééchantillonnage does not require making strong assumptions about the distribution of the population. It is a computationally intensive method but is widely used in various fields including statistics, machine learning, and economics due to its flexibility and robustness. It is a non-parametric approach, meaning it does not rely on assumptions about the shape of the population's distribution.