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[Jackknife and bootstrap].

M Chavance1

  • 1INSERM U169 16, Villejuif.

Revue D'Epidemiologie Et De Sante Publique
|January 1, 1992
PubMed
Summary
This summary is machine-generated.

The jackknife and bootstrap are non-parametric statistical methods for estimating bias and variance without distribution assumptions. They use subsampling techniques to assess estimator reliability in various applications.

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Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Non-parametric statistical methods are crucial when data distribution is unknown.
  • Estimating the bias and variance of estimators is fundamental in statistical inference.

Purpose of the Study:

  • To present and explain the jackknife and bootstrap methods.
  • To illustrate the application of these non-parametric techniques.

Main Methods:

  • The jackknife method involves calculating an estimator on subsamples of size n-1.
  • The bootstrap method involves resampling with replacement to create new samples of size n.

Main Results:

  • Both methods provide robust estimates of bias and variance.
  • Demonstrated applicability to simple and complex epidemiological problems.

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Conclusions:

  • Jackknife and bootstrap are versatile non-parametric tools for statistical inference.
  • These methods are valuable for assessing estimator performance without distributional assumptions.