In phylogenetic analysis: Perform analysis x number of times, with subtle randomization of the input data. The the number of times a particular branch is formed in the tree (out of the x times) can be used to estimate its probability, which can be indicated on a consensus tree.
A statistical method for placing some form of confidence limits on a set of observations without making too many assumptions. It is therefore well-suited to the analysis of phylogenetic trees.
Training data sets are created by re-sampling with replacement from the original training set, so data records may occur more than once. In other words, this method treats a sample as if it were the entire population. Usually, final estimates are obtained by taking the average of the estimates from each of the bootstrap test sets.
A technique for estimating the reliability of an internal branch of a tree by resampling the original data set. With DNA sequences the bases at each position are randomly sampled then returned to the pool so that they may be resampled again. The bootstrap value for a branch is the percentage of such resamplings (typically 500 to 1000) that recover the branch. Compare Jackknifing in which resampling is done without replacement, and Parametric bootstrapping in which the sequences to be resampled are generated by numerical simulation.
(Paleoanthropology, Cladistics) ??? Getting the data re-analysing looking for branch and bound results.
A simulation method for deriving nonparametric estimates of variables of interest from a data set.
Is the technique the initiate a sample or process. It can use a piece of data to generate or infer other data. These other data are not necessarily observations.
A computer-based resampling method for estimating sampling variances, confidence intervals, stability of regression models, and other properties of statistics.
Bootstrapping is a way of testing the reliability of the dataset. It is the creation of pseudoreplicate datasets by resampling. Bootstrapping allows you to assess whether the distribution of characters has been influenced by stochastic effects.
In statistics, bootstrapping is a method for estimating the sampling distribution of an estimator by resampling with replacement from the original sample. It is distinguished from the jackknife procedure, used to detect outliers, and cross-validation, used to make sure that results are repeatable. There are more complicated bootstraps for sampling without replacement, two-sample problems, regression, time series, hierarchical sampling, mediation analyses, and other statistical problems.