@article { ISI:000319173400020,
	title = {Robust and efficient estimation of the residual scale in linear regression},
	journal = {JOURNAL OF MULTIVARIATE ANALYSIS},
	volume = {116},
	year = {2013},
	month = {APR},
	pages = {278-296},
	publisher = {ELSEVIER INC},
	type = {Article},
	address = {525 B STREET, STE 1900, SAN DIEGO, CA 92101-4495 USA},
	abstract = {Robustness and efficiency of the residual scale estimators in the regression model is important for robust inference. We introduce the class of robust generalized M-scale estimators for the regression model, derive their influence function and gross-error sensitivity, and study their maxbias behavior. In particular, we find overall minimax bias estimates for the general class and also for well-known subclasses. We pose and solve a Hampers-like optimality problem: we find generalized M-scale estimators with maximal efficiency subject to a lower bound on the global and local robustness of the estimators. (C) 2013 Elsevier Inc. All rights reserved.},
	keywords = {Efficiency, Gross-error sensitivity, influence function, Maxbias, Robust scale},
	issn = {0047-259X},
	doi = {10.1016/j.jmva.2012.12.008},
	author = {Van Aelst, Stefan and Willems, Gert and Zamar, Ruben H.}
}
@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.}
}
