@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.}
}
@article { ISI:000365144600020,
	title = {S-Estimators for Functional Principal Component Analysis},
	journal = {JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION},
	volume = {110},
	number = {511},
	year = {2015},
	month = {SEP},
	pages = {1100-1111},
	publisher = {AMER STATISTICAL ASSOC},
	type = {Article},
	address = {732 N WASHINGTON ST, ALEXANDRIA, VA 22314-1943 USA},
	abstract = {<p>Principal component analysis is a widely used technique that provides an optimal lower-dimensional approximation to multivariate or functional datasets. These approximations can be very useful in identifying potential outliers among high-dimensional or functional observations. In this article, we propose a new class of estimators for principal components based on robust scale estimators. For a fixed dimension q, we robustly estimate the q-dimensional linear space that provides the best prediction for the data, in the sense of minimizing the sum of robust scale estimators of the coordinates of the residuals. We also study an extension to the infinite-dimensional case. Our method is consistent for elliptical random vectors, and is Fisher consistent for elliptically distributed random elements on arbitrary Hilbert spaces. Numerical experiments show that our proposal is highly competitive when compared with other methods. We illustrate our approach on a real dataset, where the robust estimator discovers atypical observations that would have been missed otherwise. Supplementary materials for this article are available online.</p>
},
	keywords = {Functional data analysis, Robust estimation, Sparse data},
	issn = {0162-1459},
	doi = {10.1080/01621459.2014.946991},
	author = {Boente, Graciela and Salibian-Barrera, Mat{\'\i}as}
}
@article { ISI:000329285700012,
	title = {A Natural Robustification of the Ordinary Instrumental Variables Estimator},
	journal = {BIOMETRICS},
	volume = {69},
	number = {3},
	year = {2013},
	month = {SEP},
	pages = {641-650},
	publisher = {WILEY-BLACKWELL},
	type = {Article},
	address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
	abstract = {Instrumental variables estimators are designed to provide consistent parameter estimates for linear regression models when some covariates are correlated with the error term. We propose a new robust instrumental variables estimator (RIV) which is a natural robustification of the ordinary instrumental variables estimator (OIV). Specifically, we construct RIV using a robust multivariate location and scatter S-estimator to robustify the solution of the estimating equations that define OIV. RIV is computationally inexpensive and readily available for applications through the R-library riv. It has attractive robustness and asymptotic properties, including high resilience to outliers, bounded influence function, consistency under weak distributional assumptions, asymptotic normality under mild regularity conditions, and equivariance. We further endow RIV with an iterative algorithm which allows for the estimation of models with endogenous continuous covariates and exogenous dummy covariates. We study the performance of RIV when the data contains outliers using an extensive Monte Carlo simulation study and by applying it to a limited-access dataset from the Framingham Heart Study-Cohort to estimate the effect of long-term systolic blood pressure on left atrial size.},
	keywords = {Endogenous covariate, instrumental variable, Robust estimation, S-estimator},
	issn = {0006-341X},
	doi = {10.1111/biom.12043},
	author = {Freue, Gabriela V. Cohen and Ortiz-Molina, Hernan and Zamar, Ruben H.}
}
@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.}
}
