@article { ISI:000359388200001,
	title = {Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination},
	journal = {TEST},
	volume = {24},
	number = {3},
	year = {2015},
	month = {SEP},
	pages = {441-461},
	publisher = {SPRINGER},
	type = {Article},
	address = {233 SPRING ST, NEW YORK, NY 10013 USA},
	abstract = {Multivariate location and scatter matrix estimation is a cornerstone in multivariate data analysis. We consider this problem when the data may contain independent cellwise and casewise outliers. Flat data sets with a large number of variables and a relatively small number of cases are common place in modern statistical applications. In these cases, global down-weighting of an entire case, as performed by traditional robust procedures, may lead to poor results. We highlight the need for a new generation of robust estimators that can efficiently deal with cellwise outliers and at the same time show good performance under casewise outliers.},
	keywords = {Cellwise contamination, Multivariate data analysis, Multivariate location and scatter, Robust estimation},
	issn = {1133-0686},
	doi = {10.1007/s11749-015-0450-6},
	author = {Agostinelli, Claudio and Leung, Andy and Yohai, Victor J. and Zamar, Ruben H.}
}
