jackafterboot {MCE}R Documentation

Jackknife-After-Bootstrap Method of MCE estimation

Description

Efron and Tibshirani have introduced a method for estimating Monte Carlo error for the estimates generated from the bootstrap. This method uses ideas similar to a jacknife method.

Usage

jackafterboot(data, B = 100, type = "", truth)

Arguments

data vector. replication results
B numeric (integer). number of bootstrap samples to be taken
type character. type of simulation result including "mean", "SE", "RE", and "PB" for mean, standard error, relative efficiency, and percent bias.
truth numeric. if type="PB" the true value.

Details

This function takes original data and perfoms the bootstrap as well as estimates the error in the bootstrap estimates. For an original data set with N points and B bootstrap samples, the first estimate is calculated using only the replicates that do not contain the first point of the data. Then the second estimate is from only the replicates that do not contain the second point, and so on until all N points have been removed. An estimate is calculated from these replications without the nth point. Then the standard deviation from the N estimates should estimate Monte Carlo error.

Value

Jackknife-After-Bootstrap estimate of MCE

Note

Efron notes that this technique runs into trouble if one point appears in all B bootstrap samples, but claims this is rare when N >= 10 and B >= 20.

Author(s)

Elizabeth Koehler

References

Efron, B. and R. Tibshirani (1993). underline{An Introduction to the Bootstrap}. New York: Chapman & Hall.

Examples

data1<-rnorm(1000)
jackafterboot(data1,100, type="mean")

     
      


[Package MCE version 1.0 Index]