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(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 validity of a single sample.
In bootstrap-resamples, the 'population' is in fact the sample, and this is known; hence the quality of inference of the 'true' sample from resampled data (resampled → sample) is measurable. More formally, the bootstrap works by treating inference of the true probability distribution J , given the original data, as being analogous to an ...
Rather than building a single smoother for the complete dataset, 100 bootstrap samples were drawn. Each sample is composed of a random subset of the original data and maintains a semblance of the master set's distribution and variability. For each bootstrap sample, a LOESS smoother was fit.
In statistics, the jackknife (jackknife cross-validation) is a cross-validation technique and, therefore, a form of resampling. It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such as the bootstrap.
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.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
April 2011) (Learn how and when to remove this message) In statistics , the Lehmann–Scheffé theorem is a prominent statement, tying together the ideas of completeness, sufficiency, uniqueness, and best unbiased estimation. [ 1 ]