Plug-in principle

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In statistics, the plug-in principle [1] is the method of estimation of functionals of a population distribution by evaluating the same functionals at the empirical distribution based on a sample.

The best example of the plug-in principle, the bootstrapping method.

For example,[1] when estimating the population mean, this method uses the sample mean; to estimate the population median, it uses the sample median; to estimate the population regression line, it uses the sample regression line.

It is called a principle because it is too simple to be otherwise, it is just a guideline, not a theorem.


  1. ^ a b Logan, J. David and Wolesensky, Willian R. Mathematical methods in biology. Pure and Applied Mathematics: a Wiley-interscience Series of Texts, Monographs, and Tracts. John Wiley& Sons, Inc. 2009. Chapter 6: Statistical inference. Section 6.6: Bootstrap methods

See also

Further references

  • Wright, D.B., London, K., Field, A.P. Using Bootstrap Estimation and the Plug-in Principle for Clinical Psychology Data. 2011 Textrum Ltd. Online: Retrieved on 25/04/2016.
  • An Introduction to the Bootstrap. Monographs on Statistics and applied probability 57. Chapman&Hall/CHC. 1998. Online Retrieved on 25 04 2016.

External links

  • Lecture notes. Retrieved on 25 April 2016.
  • Lecture notes 2. Retrieved on 25 April 2016.

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