@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:000279451600006,
	title = {On a robust local estimator for the scale function in heteroscedastic nonparametric regression},
	journal = {STATISTICS \& PROBABILITY LETTERS},
	volume = {80},
	number = {15-16},
	year = {2010},
	month = {AUG 1},
	pages = {1185-1195},
	publisher = {ELSEVIER SCIENCE BV},
	type = {Article},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function and to construct robust confidence bands. In this paper, local M-estimators of the scale function based on consecutive differences of the responses, for fixed designs are considered. Under mild regularity conditions, the asymptotic behavior of the local M-estimators for general weight functions is derived. (C) 2010 Elsevier B.V. All rights reserved.},
	keywords = {Heteroscedasticity, Local M-estimators, nonparametric regression, Robust estimation},
	issn = {0167-7152},
	doi = {10.1016/j.spl.2010.03.015},
	author = {Boente, Graciela and Ruiz, Marcelo and Zamar, Ruben H.}
}
@article { ISI:000265170600008,
	title = {Bayesian likelihood robustness in linear models},
	journal = {JOURNAL OF STATISTICAL PLANNING AND INFERENCE},
	volume = {139},
	number = {7},
	year = {2009},
	month = {JUL 1},
	pages = {2196-2207},
	publisher = {ELSEVIER SCIENCE BV},
	type = {Article},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {This paper deals with the problem of robustness of Bayesian regression with respect to the data. We first give a formal definition of Bayesian robustness to data contamination, prove that robustness according to the definition cannot be obtained by using heavy-tailed error distributions in linear regression models and propose a heteroscedastic approach to achieve the desired Bayesian robustness. (C) 2008 Elsevier B.V. All rights reserved.},
	keywords = {Bayesian inference, Heteroscedasticity, Kullback-Leibler divergence, Robust regression},
	issn = {0378-3758},
	doi = {10.1016/j.jspi.2008.10.012},
	author = {Pena, Daniel and Zamar, Ruben and Yan, Guohua}
}
