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The bootstrap method is a statistical method used to obtain estimates of the uncertainty of statistics or parameters. It is based on iteratively taking samples from a given set of data and estimating statistics for each of those samples. It allows us to obtain information about the distribution of a statistic without relying on assumptions about the underlying distribution.
Here are the steps to use the bootstrap method:
Data set: given a data set with n observations.
Take samples: Substitute repeatedly takes samples of size n from the original dataset. This means that observations may appear multiple times in different samples, while others may not be selected at all.
Estimate Statistics: The desired statistics are calculated for each of the samples taken. This can be the mean, standard deviation, median, or any other statistic based on the data.
Create distribution: The results of the statistics from the samples form the bootstrap distribution. This distribution gives us information about the uncertainty of the estimate of the statistic.
Calculate statistics: Various statistical measures can be calculated based on the bootstrap distribution, e.g. B. Confidence intervals, standard errors or p-values.
The advantage of bootstrap is that it is robust to assumptions about the distribution of the data and can be applied to complex situations where analytical methods may not be available or applicable. However, it is important to note that the bootstrap procedure cannot solve all possible problems and requires careful interpretation of the results.