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Bootstrapping (statistics)

Bootstrapping is a procedure for estimating the distribution of an estimator by resampling (often with replacement) one's data or a model which is estimated from the data. Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. This technique allows estimation of the sampling…

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Overview

History

Approach

Discussion

Types of bootstrap scheme

Methods for improving computational efficiency

Choice of statistic

Deriving confidence intervals from the bootstrap distribution

Example applications

Relation to other approaches to inference

Asymptotic theory

Finite populations

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Map overview Semantic statistics

Bootstrapping (statistics)

Nodes111
Edges110
Triples0
Avg. degree1.98
Density0.018018
Components1

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Important terminology

bootstrap distribution sample displaystyle data mean bootstrapping statistic using method random samples confidence methods variance resampling one original standard replacement

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SubjectPredicateObjectConfidenceSrc

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