Sboot_loccov: Fast and Robust Bootstrap for S-estimates of...

In FRB: Fast and Robust Bootstrap

Description

Calculates bootstrapped S-estimates using the Fast and Robust Bootstrap method.

Usage

Sboot_loccov(Y, R = 999, ests = Sest_loccov(Y))

Arguments

Y	matrix or data frame.
R	number of bootstrap samples. Default is R=999.
ests	original S-estimates as returned by Sest_loccov().

Details

This function is called by FRBpcaS and FRBhotellingS, it is typically not to be used on its own. It requires the S-estimates of multivariate location and scatter/shape (the result of Sest_loccov applied on Y), supplied through the argument ests. If ests is not provided, Sest_loccov calls the implementation of the multivariate S-estimates in package rrcov of Todorov and Filzmoser (2009) with default arguments.

For multivariate data the fast and robust bootstrap was developed by Salibian-Barrera, Van Aelst and Willems (2006).

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

Value

A list containing:

centered	recalculated estimates of location and covariance (centered by original estimates)
Sest	original estimates of location and covariance

Author(s)

Gert Willems, Ella Roelant and Stefan Van Aelst

References

• M. Salibian-Barrera, S. Van Aelst and G. Willems (2006) PCA based on multivariate MM-estimators with fast and robust bootstrap. Journal of the American Statistical Association, 101, 1198–1211.

• M. Salibian-Barrera, S. Van Aelst and G. Willems (2008) Fast and robust bootstrap. Statistical Methods and Applications, 17, 41–71.

• V. Todorov and P. Filzmoser (2009) An object-oriented framework for robust multivariate analysis. Journal of Statistical Software, 32, 1–47. URL http://www.jstatsoft.org/v32/i03/.

• S. Van Aelst and G. Willems (2013). Fast and robust bootstrap for multivariate inference: The R package FRB. Journal of Statistical Software, 53(3), 1–32. URL: http://www.jstatsoft.org/v53/i03/.

See Also

FRBpcaS, FRBhotellingS, MMboot_loccov

Examples

Y <- matrix(rnorm(50*5), ncol=5)
Sests <- Sest_loccov(Y, bdp = 0.25) 
bootresult <- Sboot_loccov(Y, R = 1000, ests = Sests)

FRB documentation built on May 29, 2017, 5:45 p.m.