Jackknife resampling | Wikipedia audio article
Channel: wikipedia tts
Date: 06/10/2019

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Jackknife_resampling


00:00:04 1 Estimation
00:00:11 2 Bias estimation and correction



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SUMMARY
=======
In statistics, the jackknife is a resampling technique especially useful for variance and bias estimation. The jackknife pre-dates other common resampling methods such as the bootstrap. The jackknife estimator of a parameter is found by systematically leaving out each observation from a dataset and calculating the estimate and then finding the average of these calculations. Given a sample of size 
  
    
      
        n
      
    
    {\displaystyle n}
  , the jackknife estimate is found by aggregating the estimates of each 
  
    
      
        (
        n
        −
        1
        )
      
    
    {\displaystyle (n-1)}
  -sized sub-sample.
The jackknife technique was developed by Maurice Quenouille (1924-1973) from 1949, and refined in 1956. John Tukey expanded on the technique in 1958 and proposed the name "jackknife" since, like a physical jack-knife (a compact folding knife), it is a rough-and-ready tool that can improvise a solution for a variety of problems even though specific problems may be more efficiently solved with a purpose-designed tool.The jackknife is a linear approximation of the bootstrap.