mboot {moonboot}R Documentation

m-Out-of-n Bootstrap Implementation

Description

Generate R bootstrap replicates of the given statistic applied to the data. Sampling can be done with or without replacement. The subsample size m can either be chosen directly or estimated with estimate.m().

Usage

mboot(data, statistic, m, R = 1000, replace = FALSE, ...)

Arguments

data

The data to be bootstrapped. If it is multidimensional, each row is considered as one observation passed to the statistic.

statistic

A function returing the statistic of interest. It must take two arguments. The first argument passed will be the original data, the second will be a vector of indicies. Any further arguments can be passed through the ... argument.

m

The subsampling size.

R

The number of bootstrap replicates.

replace

Whether sampling should be done with replacement or without replacement (the default).

...

Additional parameters to be passed to the statistic.

Details

m needs to be a numeric value meeting the condition 2<=m<=n. It must be chosen such that m goes to infinity as n goes to infinits, but the ratio m/n must go to zero. The m-out-of-n Bootstrap without replacement, known as subsampling, was introduced by Politis and Romano (1994).

Value

The returned value is an object of the class "mboot" containing the following components:

References

Politis D.N. and Romano J.P. (1994) Large sample confidence regions based on subsamples under minimal assumptions. The Annals of Statistics, 22(4):2031-2050, doi:10.1214/aos/1176325770

See Also

mboot.ci estimate.m estimate.tau

Examples

data <- runif(1000)
estimate.max <- function(data, indices) {return(max(data[indices]))}
boot.out <- mboot(data, estimate.max, R = 1000, m = 2*sqrt(NROW(data)), replace = FALSE)


[Package moonboot version 1.0.1 Index]