@article { ISI:000249912400020,
	title = {Building a robust linear model with forward selection and stepwise procedures},
	journal = {COMPUTATIONAL STATISTICS \& DATA ANALYSIS},
	volume = {52},
	number = {1},
	year = {2007},
	month = {SEP 15},
	pages = {239-248},
	publisher = {ELSEVIER SCIENCE BV},
	type = {Article},
	address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
	abstract = {Classical step-by-step algorithms, such as forward selection (FS) and stepwise (SW) methods, are computationally suitable, but yield poor results when the data contain outliers and other contaminations. Robust model selection procedures, on the other hand, are not computationally efficient or scalable to large dimensions, because they require the fitting of a large number of submodels. Robust and computationally efficient versions of FS and SW are proposed. Since FS and SW can be expressed in terms of sample correlations, simple robustifications are obtained by replacing these correlations by their robust counterparts. A pairwise approach is used to construct the robust correlation matrix-not only because of its computational advantages over the d-dimensional approach, but also because the pairwise approach is more consistent with the idea of step-by-step algorithms. The proposed robust methods have much better performance compared to standard FS and SW. Also, they are computationally very suitable and scalable to large high-dimensional data sets. (c) 2007 Elsevier B.V. All rights reserved.},
	keywords = {computational complexity, pairwise robust correlation, robust model selection, stepwise algorithm},
	issn = {0167-9473},
	doi = {10.1016/j.csda.2007.01.007},
	author = {Khan, Jafar A. and Van Aelst, Stefan and Zamar, Ruben H.}
}
@article { ISI:000251829200023,
	title = {Robust linear model selection based on least angle regression},
	journal = {JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION},
	volume = {102},
	number = {480},
	year = {2007},
	month = {DEC},
	pages = {1289-1299},
	publisher = {AMER STATISTICAL ASSOC},
	type = {Article},
	address = {1429 DUKE ST, ALEXANDRIA, VA 22314 USA},
	abstract = {In this article we consider the problem of building a linear prediction model when the number of candidate predictors is large and the data possibly contain anomalies that are difficult to visualize and clean. We want to predict the nonoutlying cases; therefore, we need a method that is simultaneously robust and scalable. We consider the stepwise least angle regression (LARS) algorithm which is computationally very efficient but sensitive to outliers. We introduce two different approaches to robustify LARS. The plug-in approach replaces the classical correlations in LARS by robust correlation estimates. The cleaning approach first transforms the data set by shrinking the outliers toward the bulk of the data (which we call multivariate Winsorization) and then applies LARS to the transformed data. We show that the plug in approach is time-efficient and scalable and that the bootstrap can be used to stabilize its results. We recommend using bootstrapped robustified LARS to sequence a number of candidate predictors to form a reduced set from which a more refined model can be selected.},
	keywords = {bootstrap, computational complexity, robust prediction, stepwise algorithm, Winsorization},
	issn = {0162-1459},
	doi = {10.1198/016214507000000950},
	author = {Khan, Jafar A. and Van Aelst, Stefan and Zamar, Ruben H.}
}
