@article { ISI:000238028100013,
	title = {Bootstrapping MM-estimators for linear regression with fixed designs},
	journal = {STATISTICS \& PROBABILITY LETTERS},
	volume = {76},
	number = {12},
	year = {2006},
	month = {JUL 1},
	pages = {1287-1297},
	publisher = {ELSEVIER SCIENCE BV},
	type = {Article},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {In this paper, I study the extension of the robust bootstrap [Salibian-Barrera, M., Zarnar, R.H., 2002. Bootstrapping robust estimates of regression. Ann. Statist. 30, 556-582] to the case of fixed designs. The robust bootstrap is a computer-intensive inference method for robust regression estimators which is computationally simple (because we do not need to recompute the robust estimate with each bootstrap sample) and robust to the presence of outliers in the bootstrap samples. In this paper, I prove the consistency of this method for the case of non-random explanatory variables and illustrate its use on a real data set. Simulation results indicate that confidence intervals based on the robust bootstrap have good finite-sample coverage levels. (C) 2006 Elsevier B.V. All rights reserved.},
	keywords = {bootstrap, fixed design, inference, linear regression, MM-estimators, Robustness},
	issn = {0167-7152},
	doi = {10.1016/j.spl.2006.01.008},
	author = {Salibian-Barrera, M}
}
