@article { ISI:000186911900009,
	title = {Globally robust inference for the location and simple linear regression models},
	journal = {JOURNAL OF STATISTICAL PLANNING AND INFERENCE},
	volume = {119},
	number = {2},
	year = {2004},
	month = {FEB 1},
	pages = {353-375},
	publisher = {ELSEVIER SCIENCE BV},
	type = {Article},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {We define globally robust confidence intervals and p-values for the location and simple linear regression models. The need for robust inference has been noticed and partially addressed in the statistical literature (see for example the book by Barnett and Lewis, Outliers in Statistical Data, Wiley, New York, 1994 and references therein). We construct intervals that are stable in the sense of achieving coverages near the nominal ones even in the presence of outliers and other departures from the parametric model. Moreover, our intervals are informative in the sense of having relatively short lengths. These globally robust confidence intervals constitute an improvement over previous robust intervals which do not take into account the potential bias of the estimates. (C) 2002 Elsevier B.V. All rights reserved.},
	keywords = {linear regerssion, maximum bias, robust confidence intervals, robust interference, Robustness},
	issn = {0378-3758},
	doi = {10.1016/S0378-3758(02)00490-1},
	author = {Adrover, J and Salibian-Barrera, M and Zamar, R}
}
