@article { ISI:000263129000011,
	title = {PROPAGATION OF OUTLIERS IN MULTIVARIATE DATA},
	journal = {ANNALS OF STATISTICS},
	volume = {37},
	number = {1},
	year = {2009},
	month = {FEB},
	pages = {311-331},
	publisher = {INST MATHEMATICAL STATISTICS},
	type = {Article},
	address = {3163 SOMERSET DR, CLEVELAND, OH 44122 USA},
	abstract = {We investigate the performance of robust estimates of multivariate location under nonstandard data contamination models such as componentwise outliers (i.e., contamination in each variable is independent from the other variables). This model brings up a possible new source of statistical error that we call {\textquoteleft}{\textquoteleft}propagation of outliers.{\textquoteright}{\textquoteright} This source of error is Unusual in the sense that it is generated by the data processing itself and takes place after the data has been collected. We define and derive the influence function of robust multivariate location estimates under flexible contamination models and use it to investigate the effect of propagation of outliers. Furthermore, we show that standard high-breakdown affine equivariant estimators propagate outliers and therefore show poor breakdown behavior under componentwise contamination when the dimension d is high.},
	keywords = {breakdown point, contamination model, independent contamination, influence function, Robustness},
	issn = {0090-5364},
	doi = {10.1214/07-AOS588},
	author = {Alqallaf, Fatemah and Van Aelst, Stefan and Yohai, Victor J. and Zamar, Ruben H.}
}
@article { ISI:000175912300015,
	title = {Bootstrapping robust estimates of regression},
	journal = {ANNALS OF STATISTICS},
	volume = {30},
	number = {2},
	year = {2002},
	month = {APR},
	pages = {556-582},
	publisher = {INST MATHEMATICAL STATISTICS},
	type = {Article},
	address = {PO BOX 22718, BEACHWOOD, OH 44122 USA},
	abstract = {We introduce a new computer-intensive method to estimate the distribution of robust regression estimates. The basic idea behind Our method is to bootstrap a reweighted representation of the estimates. To obtain a bootstrap method that is asymptotically correct, we include the auxiliary scale estimate in our reweighted representation of the estimates. Our method is computationally simple because for each bootstrap sample we only have to solve a linear system of equations. The weights we use are decreasing functions of the absolute value of the residuals and hence outlying observations receive small weights. This results in a bootstrap method that is resistant to the presence of outliers in the data. The breakdown points of the quantile estimates derived with this method are higher than those obtained with the bootstrap. We illustrate our method on two datasets and we report the results of a Monte Carlo experiment on confidence intervals for the parameters of the linear model.},
	keywords = {breakdown point, confidence intervals, regression},
	issn = {0090-5364},
	doi = {10.1214/aos/1021379865},
	author = {Salibian-Barrera, M and Zamar, RH}
}
