@article { ISI:000302033200021,
	title = {Bandwidth choice for robust nonparametric scale function estimation},
	journal = {COMPUTATIONAL STATISTICS \& DATA ANALYSIS},
	volume = {56},
	number = {6},
	year = {2012},
	month = {JUN},
	pages = {1594-1608},
	publisher = {ELSEVIER SCIENCE BV},
	type = {Article},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {We introduce and compare several robust procedures for bandwidth selection when estimating the variance function. These bandwidth selectors are to be used in combination with the robust scale estimates introduced by Boente et al. (2010a). We consider two different robust cross-validation strategies combined with two ways for measuring the cross-validation prediction error. The different proposals are compared with non robust alternatives using Monte Carlo simulation. We also derive some asymptotic results to investigate the large sample performance of the corresponding robust data-driven scale estimators. (C) 2011 Elsevier B.V. All rights reserved.},
	keywords = {Cross-validation, Data-driven bandwidth, Heteroscedasticity, Local M-estimators, nonparametric regression, Robust estimation},
	issn = {0167-9473},
	doi = {10.1016/j.csda.2011.10.002},
	author = {Boente, Graciela and Ruiz, Marcelo and Zamar, Ruben H.}
}
@article { ISI:000281333900020,
	title = {Fast robust estimation of prediction error based on resampling},
	journal = {COMPUTATIONAL STATISTICS \& DATA ANALYSIS},
	volume = {54},
	number = {12, SI},
	year = {2010},
	month = {DEC 1},
	pages = {3121-3130},
	publisher = {ELSEVIER SCIENCE BV},
	type = {Article},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {Robust estimators of the prediction error of a linear model are proposed. The estimators are based on the resampling techniques cross-validation and bootstrap. The robustness of the prediction error estimators is obtained by robustly estimating the regression parameters of the linear model and by trimming the largest prediction errors. To avoid the recalculation of time-consuming robust regression estimates, fast approximations for the robust estimates of the resampled data are used. This leads to time-efficient and robust estimators of prediction error. (C) 2010 Elsevier B.V. All rights reserved.},
	keywords = {bootstrap, Cross-validation, Prediction error, Robustness},
	issn = {0167-9473},
	doi = {10.1016/j.csda.2010.01.031},
	author = {Khan, Jafar A. and Van Aelst, Stefan and Zamar, Ruben H.}
}
