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A key result in Efron's seminal paper that introduced the bootstrap [4] is the favorable performance of bootstrap methods using sampling with replacement compared to prior methods like the jackknife that sample without replacement. However, since its introduction, numerous variants on the bootstrap have been proposed, including methods that ...
Please help improve it to make it understandable to non-experts, without removing the technical details. ( March 2011 ) ( Learn how and when to remove this message ) In statistics , the bootstrap error-adjusted single-sample technique ( BEST or the BEAST ) is a non-parametric method that is intended to allow an assessment to be made of the ...
As non-parametric methods make fewer assumptions, their applicability is much more general than the corresponding parametric methods. In particular, they may be applied in situations where less is known about the application in question. Also, due to the reliance on fewer assumptions, non-parametric methods are more robust.
The best example of the plug-in principle, the bootstrapping method. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio ...
That is, no parametric equation is assumed for the relationship between predictors and dependent variable. Nonparametric regression requires larger sample sizes than regression based on parametric models because the data must supply the model structure as well as the parameter estimates.
One method of generating them is based on the Binomial distribution. Considering a single point of a CDF of value F ( x i ) {\displaystyle F(x_{i})} , then the empirical distribution at that point will be distributed proportional to the binomial distribution with p = F ( x i ) {\displaystyle p=F(x_{i})} and n {\displaystyle n} set equal to the ...
Non-parametric bootstrapping is a time-consuming process that scales linearly with the number of replicates, since every bootstrap sample is processed by the same method as the original tree, and post-processing steps are required to enumerate clades.
An alternative to explicitly modelling the heteroskedasticity is using a resampling method such as the wild bootstrap. Given that the studentized bootstrap, which standardizes the resampled statistic by its standard error, yields an asymptotic refinement, [13] heteroskedasticity-robust standard errors remain nevertheless useful.