arw: Adaptive reweighted estimator for multivariate location and...

Description Usage Arguments Details Value Author(s) References Examples

Description

Adaptive reweighted estimator for multivariate location and scatter with hard-rejection weights. The multivariate outliers are defined according to the supremum of the difference between the empirical distribution function of the robust Mahalanobis distance and the theoretical distribution function.

Usage

1
arw(x, m0, c0, alpha, pcrit)

Arguments

x

Dataset (n x p)

m0

Initial location estimator (1 x p)

c0

Initial scatter estimator (p x p)

alpha

Maximum thresholding proportion (optional scalar, default: alpha = 0.025)

pcrit

Critical value obtained by simulations (optional scalar, default value obtained from simulations)

Details

At the basis of initial estimators of location and scatter, the function arw performs a reweighting step to adjust the threshold for outlier rejection. The critical value pcrit was obtained by simulations using the MCD estimator as initial robust covariance estimator. If a different estimator is used, pcrit should be changed and computed by simulations for the specific dimensions of the data x.

Value

m

Adaptive location estimator (p x 1)

c

Adaptive scatter estimator (p x p)

cn

Adaptive threshold ("adjusted quantile")

w

Weight vector (n x 1)

Author(s)

Moritz Gschwandtner <e0125439@student.tuwien.ac.at>
Peter Filzmoser <P.Filzmoser@tuwien.ac.at> http://cstat.tuwien.ac.at/filz/

References

P. Filzmoser, R.G. Garrett, and C. Reimann (2005). Multivariate outlier detection in exploration geochemistry. Computers & Geosciences, 31:579-587.

Examples

1
2
x <- cbind(rnorm(100), rnorm(100))
arw(x, apply(x,2,mean), cov(x))

StatDA documentation built on March 13, 2020, 2:42 a.m.

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