# Exploring the Jackknife technique

This method is used to estimate characteristics such as the distortion and the standard deviation of a statistic. This technique allows us to obtain the desired estimates without necessarily resorting to parametric assumptions. Jackknife is based on calculating the statistics of interest for the sub-samples we've obtained, leaving out one sample observation at a time. The jackknife estimate is consistent for various sample statistics, such as mean, variance, correlation coefficient, maximum likelihood estimator, and others.

## Defining the Jackknife method

The Jackknife method was proposed in 1949 by M. H. Quenouille who, due to the low computational power of the time, created an algorithm that requires a fixed number of accounts.

Important Note

The main idea behind this method is to cut a different observation from the original sample each time and to re-evaluate the parameter of interest. The estimate will be compared with the same...