MMboot_loccov {FRB}R Documentation

Fast and Robust Bootstrap for MM-estimates of Location and Covariance

Description

Calculates bootstrapped MM-estimates of multivariate location and scatter using the Fast and Robust Bootstrap method.

Usage

MMboot_loccov(Y, R, ests = MMest_loccov(Y))

Arguments

Y matrix or data frame
R number of bootstrap samples
ests original MM-estimates as returned by MMest_loccov()

Details

This function is called by FRBpcaMM and FRBhotellingMM, it is typically not to be used on its own. It requires the result of MMest_loccov applied on Y, supplied through the argument ests. If ests is not provided, MMest_loccov will be called with default arguments.

The fast and robust bootstrap was first developed by Salibian-Barrera and Zamar (2002) for univariate regression MM-estimators.

The value centered gives a matrix with R columns and 2*(p+p*p) rows (p is the number of variables in Y), containing the recalculated estimates of the MM-location, MM-shape, S-covariance and S-location. Each column represents a different bootstrap sample. The first p rows are the MM-location estimates, the next p*p rows are the MM-shape estimates (vectorized). Then the next p*p rows are the S-covariance estimates (vectorized) and the final p rows are the S-location estimates. The estimates are centered by the original estimates, which are also returned through MMest in vectorized form.

Value

A list containing:

centered recalculated MM- and S-estimates of location and scatter (centered by original estimates), see Details
MMest original MM- and S-estimates of location and scatter, see Details

Author(s)

Gert Willems and Ella Roelant

References

See Also

FRBpcaMM, FRBhotellingMM, MMest_loccov, Sboot_loccov

Examples


Y <- matrix(rnorm(50*5), ncol=5)
MMests <- MMest_loccov(Y) 
bootresult <- MMboot_loccov(Y, R = 1000, ests = MMests)


[Package FRB version 1.4 Index]