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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 validity of a single sample.
The studentized bootstrap, also called bootstrap-t, is computed analogously to the standard confidence interval, but replaces the quantiles from the normal or student approximation by the quantiles from the bootstrap distribution of the Student's t-test (see Davison and Hinkley 1997, equ. 5.7 p. 194 and Efron and Tibshirani 1993 equ 12.22, p. 160):
Cross-validation is employed repeatedly in building decision trees. One form of cross-validation leaves out a single observation at a time; this is similar to the jackknife. Another, K-fold cross-validation, splits the data into K subsets; each is held out in turn as the validation set. This avoids "self-influence".
Minimum message length (MML) is a Bayesian information-theoretic method for statistical model comparison and selection. [1] It provides a formal information theory restatement of Occam's Razor: even when models are equal in their measure of fit-accuracy to the observed data, the one generating the most concise explanation of data is more likely to be correct (where the explanation consists of ...
April 2013) (Learn how and when to remove this message) Bennett, Alpert & Goldstein’s S is a statistical measure of inter-rater agreement . It was created by Bennett et al. in 1954.
(October 2021) (Learn how and when to remove this message) ( Learn how and when to remove this message ) An exact (significance) test is a statistical test such that if the null hypothesis is true, then all assumptions made during the derivation of the distribution of the test statistic are met.
Efron is especially known for proposing the bootstrap resampling technique, [3] which has had a major impact in the field of statistics and virtually every area of statistical application. The bootstrap was one of the first computer-intensive statistical techniques, replacing traditional algebraic derivations with data-based computer simulations.
David Amiel Freedman (5 March 1938 – 17 October 2008) was a Professor of Statistics at the University of California, Berkeley.He was a distinguished mathematical statistician whose wide-ranging research included the analysis of martingale inequalities, Markov processes, de Finetti's theorem, consistency of Bayes estimators, sampling, the bootstrap, and procedures for testing and evaluating ...