Title: Robust linear model selection based on least angle regression
Authors: Khan, Jafar A ×
Van Aelst, Stefan
Zamar, Ruben H #
Issue Date: 2007
Series Title: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION vol:102 issue:480 pages:1289-1299
Abstract: In this paper we consider the problem of building a linear prediction model
when the number of candidate predictors is large and the data possibly contains
anomalies that are difficult to visualize and clean. We aim at predicting the non-outlying cases. Therefore, we need a method that is robust and scalable at the
same time. We consider the stepwise algorithm LARS which is computationally
very efficient but sensitive to outliers. We introduce two different approaches
to robustify LARS. The
approach replaces the classical correlations in
LARS by robust correlation estimates. The
approach first transforms
the dataset 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 the use
of bootstrapped robustified LARS to sequence a number of candidate predictors
to form a
reduced set
from which a more refined model can be selected.
ISSN: 0162-1459
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

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