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  2. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy ( bias, variance, confidence intervals, prediction error, etc.) to sample estimates.

  3. Bootstrapping populations - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_populations

    Bootstrapping populations in statistics and mathematics starts with a sample {, …,} observed from a random variable.. When X has a given distribution law with a set of non fixed parameters, we denote with a vector , a parametric inference problem consists of computing suitable values – call them estimates – of these parameters precisely on the basis of the sample.

  4. Robust statistics - Wikipedia

    en.wikipedia.org/wiki/Robust_statistics

    Robust statistics are statistics that maintain their properties even if the underlying distributional assumptions are incorrect. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters. One motivation is to produce statistical methods that are not unduly affected by ...

  5. Bootstrap (front-end framework) - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_(front-end...

    Bootstrap (formerly Twitter Bootstrap) is a free and open-source CSS framework directed at responsive, mobile-first front-end web development. It contains HTML, CSS and (optionally) JavaScript -based design templates for typography, forms, buttons, navigation, and other interface components. As of May 2023, Bootstrap is the 17th most starred ...

  6. Bootstrap aggregating - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_aggregating

    e. Bootstrap aggregating, also called bagging (from b ootstrap agg regat ing ), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting.

  7. Statistical inference - Wikipedia

    en.wikipedia.org/wiki/Statistical_inference

    v. t. e. Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. [1] Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population.

  8. Statistics education - Wikipedia

    en.wikipedia.org/wiki/Statistics_education

    Statistics education. Statistics education is the practice of teaching and learning of statistics, along with the associated scholarly research. Statistics is both a formal science and a practical theory of scientific inquiry, and both aspects are considered in statistics education. Education in statistics has similar concerns as does education ...

  9. Source attribution - Wikipedia

    en.wikipedia.org/wiki/Source_attribution

    The frequency at which a given clade is observed in the bootstrap sample of trees quantifies the reproducibility of that node in the original tree. 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 ...